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Tianlang_Chen_Factual_or_Emotional_ECCV_2018_paper | ``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention | [
"Tianlang Chen",
"Zhongping Zhang",
"Quanzeng You",
"Chen Fang",
"Zhaowen Wang",
"Hailin Jin",
"Jiebo Luo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tianlang_Chen_Factual_or_Emotional_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tianlang_Chen_Factual_or_Emotional_ECCV_2018_paper.pdf | null | null | 1807.03871 | title_snapshot | Generating stylized captions for an image is an emerging topic in image captioning. Given an image as input, it requires the system to generate a caption that has a specific style (e.g., humorous, romantic, positive, and negative) while describing the image content semantically accurately. In this paper, we propose a n... |
Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper | Deblurring Natural Image Using Super-Gaussian Fields | [
"Yuhang Liu",
"Wenyong Dong",
"Dong Gong",
"Lei Zhang",
"Qinfeng Shi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper.pdf | null | null | null | null | Blind image deblurring is a challenging problem due to its ill-posed nature, of which the success is closely related to a proper image prior. Although a large number of sparsity-based priors, such as the sparse gradient prior, have been successfully applied for blind image deblurring, they inherently suffer from severa... |
Zhenfeng_Fan_Dense_Semantic_and_ECCV_2018_paper | Dense Semantic and Topological Correspondence of 3D Faces without Landmarks | [
"Zhenfeng Fan",
"Xiyuan Hu",
"Chen Chen",
"Silong Peng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhenfeng_Fan_Dense_Semantic_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhenfeng_Fan_Dense_Semantic_and_ECCV_2018_paper.pdf | null | null | null | null | Many previous literatures use landmarks to guide the cor- respondence of 3D faces. However, these landmarks, either manually or automatically annotated, are hard to define consistently across differ- ent faces in many circumstances. We propose a general framework for dense correspondence of 3D faces without landmarks i... |
NIKOLAOS_ZIOULIS_OmniDepth_Dense_Depth_ECCV_2018_paper | OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas | [
"Nikolaos Zioulis",
"Antonis Karakottas",
"Dimitrios Zarpalas",
"Petros Daras"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/NIKOLAOS_ZIOULIS_OmniDepth_Dense_Depth_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/NIKOLAOS_ZIOULIS_OmniDepth_Dense_Depth_ECCV_2018_paper.pdf | null | null | 1807.09620 | title_snapshot | Recent work on depth estimation up to now has only focused on projective images ignoring 360 content which is now increasingly and more easily produced. We show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training di... |
Meng_Tang_On_Regularized_Losses_ECCV_2018_paper | On Regularized Losses for Weakly-supervised CNN Segmentation | [
"Meng Tang",
"Federico Perazzi",
"Abdelaziz Djelouah",
"Ismail Ben Ayed",
"Christopher Schroers",
"Yuri Boykov"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Meng_Tang_On_Regularized_Losses_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Meng_Tang_On_Regularized_Losses_ECCV_2018_paper.pdf | null | null | 1803.09569 | title_snapshot | Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segmentation currently dominated by methods mimicking full supervision via ``fake'' fully-labeled masks (proposals) generated from available partia... |
Tianyu_Yang_Learning_Dynamic_Memory_ECCV_2018_paper | Learning Dynamic Memory Networks for Object Tracking | [
"Tianyu Yang",
"Antoni B. Chan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tianyu_Yang_Learning_Dynamic_Memory_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tianyu_Yang_Learning_Dynamic_Memory_ECCV_2018_paper.pdf | null | null | 1803.07268 | title_snapshot | Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object’s appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic m... |
Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper | Zero-Shot Deep Domain Adaptation | [
"Kuan-Chuan Peng",
"Ziyan Wu",
"Jan Ernst"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper.pdf | null | null | 1707.01922 | title_snapshot | Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-releva... |
Benjamin_Coors_SphereNet_Learning_Spherical_ECCV_2018_paper | SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images | [
"Benjamin Coors",
"Alexandru Paul Condurache",
"Andreas Geiger"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Benjamin_Coors_SphereNet_Learning_Spherical_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Benjamin_Coors_SphereNet_Learning_Spherical_ECCV_2018_paper.pdf | null | null | null | null | Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of view is essential, such as in virtual reality applications or in autonomous robots. Unfortunately, standard convolutional neural networks are not well suited for this scenario as the natural projection surface is a sphere which... |
Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper | Graininess-Aware Deep Feature Learning for Pedestrian Detection | [
"Chunze Lin",
"Jiwen Lu",
"Gang Wang",
"Jie Zhou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing pedestrian detection methods which only consider low resolution feature maps, we incorporate fine-grained information into convolutional features to make them more discriminative for human body parts... |
Long_Zhao_Learning_to_Forecast_ECCV_2018_paper | Learning to Forecast and Refine Residual Motion for Image-to-Video Generation | [
"Long Zhao",
"Xi Peng",
"Yu Tian",
"Mubbasir Kapadia",
"Dimitris Metaxas"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.pdf | null | null | 1807.09951 | title_snapshot | We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object. Recent methods for such problems typically train transformation networks to generate future frames conditioned on the structure sequence. Parallel work has shown that sho... |
Xiaopeng_Zhang_ML-LocNet_Improving_Object_ECCV_2018_paper | ML-LocNet: Improving Object Localization with Multi-view Learning Network | [
"Xiaopeng Zhang",
"Yang Yang",
"Jiashi Feng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaopeng_Zhang_ML-LocNet_Improving_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaopeng_Zhang_ML-LocNet_Improving_Object_ECCV_2018_paper.pdf | null | null | null | null | This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We propose a Multi-view Learning Localization Network (ML-LocNet) by incorporating multi-view learning into a two-phase WSOL model. The multi-view learning would benefit localization due to the complementary relationshi... |
Kaicheng_Yu_Statistically-motivated_Second-order_Pooling_ECCV_2018_paper | Statistically-motivated Second-order Pooling | [
"Kaicheng Yu",
"Mathieu Salzmann"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kaicheng_Yu_Statistically-motivated_Second-order_Pooling_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kaicheng_Yu_Statistically-motivated_Second-order_Pooling_ECCV_2018_paper.pdf | null | null | 1801.07492 | title_snapshot | However, the nature of such operations is usually computationally expensive, and resulting vector representation orders of magnitude larger than first-order baselines. Here, by contrast, we introduce a statistically-motivated framework that projects the second-order descriptor into a compact vector while improving the ... |
Zhirong_Wu_Improving_Embedding_Generalization_ECCV_2018_paper | Improving Generalization via Scalable Neighborhood Component Analysis | [
"Zhirong Wu",
"Alexei A. Efros",
"Stella X. Yu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhirong_Wu_Improving_Embedding_Generalization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhirong_Wu_Improving_Embedding_Generalization_ECCV_2018_paper.pdf | null | null | 1808.04699 | title_snapshot | Current visual recognition is dominated by the end-to-end formulation of classification problems implemented by the parametric softmax classifiers. Such formulation makes a closed world assumption with a fixed set of categories. This becomes problematic for open-set scenarios where new categories are encountered with v... |
YuKang_Gan_Monocular_Depth_Estimation_ECCV_2018_paper | Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement | [
"Yukang Gan",
"Xiangyu Xu",
"Wenxiu Sun",
"Liang Lin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/YuKang_Gan_Monocular_Depth_Estimation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/YuKang_Gan_Monocular_Depth_Estimation_ECCV_2018_paper.pdf | null | null | null | null | While significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent methods. To overcome this limitation, we expl... |
Zhongzheng_Ren_Learning_to_Anonymize_ECCV_2018_paper | Learning to Anonymize Faces for Privacy Preserving Action Detection | [
"Zhongzheng Ren",
"Yong Jae Lee",
"Michael S. Ryoo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhongzheng_Ren_Learning_to_Anonymize_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhongzheng_Ren_Learning_to_Anonymize_ECCV_2018_paper.pdf | null | null | 1803.11556 | title_snapshot | There is an increasing concern in computer vision devices invading the privacy of their users. We want the camera systems/robots to recognize important events and assist human daily life by understanding its videos, but we also want to ensure that they do not intrude people's privacy. In this paper, we propose a new pr... |
Zheng_Zhu_Distractor-aware_Siamese_Networks_ECCV_2018_paper | Distractor-aware Siamese Networks for Visual Object Tracking | [
"Zheng Zhu",
"Qiang Wang",
"Bo Li",
"Wei Wu",
"Junjie Yan",
"Weiming Hu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zheng_Zhu_Distractor-aware_Siamese_Networks_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zheng_Zhu_Distractor-aware_Siamese_Networks_ECCV_2018_paper.pdf | null | null | 1808.06048 | title_snapshot | Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, w... |
Yang_Shi_Question_Type_Guided_ECCV_2018_paper | Question Type Guided Attention in Visual Question Answering | [
"Yang Shi",
"Tommaso Furlanello",
"Sheng Zha",
"Animashree Anandkumar"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yang_Shi_Question_Type_Guided_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yang_Shi_Question_Type_Guided_ECCV_2018_paper.pdf | null | null | 1804.02088 | title_snapshot | Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question... |
Chieh_Lin_Escaping_from_Collapsing_ECCV_2018_paper | Escaping from Collapsing Modes in a Constrained Space | [
"Chia-Che Chang",
"Chieh Hubert Lin",
"Che-Rung Lee",
"Da-Cheng Juan",
"Wei Wei",
"Hwann-Tzong Chen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chieh_Lin_Escaping_from_Collapsing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chieh_Lin_Escaping_from_Collapsing_ECCV_2018_paper.pdf | null | null | 1808.07258 | title_snapshot | Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training. We study the issue of mode collapse of Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of the state-of-the-art generative models. Despite its potential of generating high-quality images, we ... |
Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper | Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-based Modeling | [
"Hiroaki Santo",
"Michael Waechter",
"Masaki Samejima",
"Yusuke Sugano",
"Yasuyuki Matsushita"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper.pdf | null | null | null | null | We present a practical method for geometric point light source calibration. Unlike in prior works that use Lambertian spheres, mirror spheres, or mirror planes, our calibration target consists of a Lambertian plane and small shadow casters at unknown positions above the plane. Due to their small size, the casters' shad... |
Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper | Bayesian Semantic Instance Segmentation in Open Set World | [
"Trung Pham",
"Vijay B. G. Kumar",
"Thanh-Toan Do",
"Gustavo Carneiro",
"Ian Reid"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper.pdf | null | null | 1806.00911 | title_snapshot | This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of existing semantic instance segmentation methods requires annotation masks for all object instances, which is expensive to acquire or even infe... |
Thomas_Robert_HybridNet_Classification_and_ECCV_2018_paper | HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning | [
"Thomas Robert",
"Nicolas Thome",
"Matthieu Cord"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Thomas_Robert_HybridNet_Classification_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Thomas_Robert_HybridNet_Classification_and_ECCV_2018_paper.pdf | null | null | 1807.11407 | title_snapshot | In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The s... |
Eddy_Ilg_Uncertainty_Estimates_and_ECCV_2018_paper | Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow | [
"Eddy Ilg",
"Ozgun Cicek",
"Silvio Galesso",
"Aaron Klein",
"Osama Makansi",
"Frank Hutter",
"Thomas Brox"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Eddy_Ilg_Uncertainty_Estimates_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Eddy_Ilg_Uncertainty_Estimates_and_ECCV_2018_paper.pdf | null | null | 1802.07095 | title_snapshot | Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make such networks estimate their local uncertainty about the correctness of their prediction, which is vital in... |
Chao_Wang_Discriminative_Region_Proposal_ECCV_2018_paper | Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation | [
"Chao Wang",
"Haiyong Zheng",
"Zhibin Yu",
"Ziqiang Zheng",
"Zhaorui Gu",
"Bing Zheng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chao_Wang_Discriminative_Region_Proposal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chao_Wang_Discriminative_Region_Proposal_ECCV_2018_paper.pdf | null | null | 1711.09554 | title_snapshot | Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it's still very challenging for translation tasks that require high quality, especially at high-resolution and photorealism. In this paper, we present Discriminative Region Proposal Adversarial Network... |
Weiwei_Shi_Transductive_Semi-Supervised_Deep_ECCV_2018_paper | Transductive Semi-Supervised Deep Learning using Min-Max Features | [
"Weiwei Shi",
"Yihong Gong",
"Chris Ding",
"Zhiheng MaXiaoyu Tao",
"Nanning Zheng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Weiwei_Shi_Transductive_Semi-Supervised_Deep_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Weiwei_Shi_Transductive_Semi-Supervised_Deep_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models. The method applies transductive learning principle to DCNN training, introduces confidence levels on unlabeled image samples to overcome unreliable label es... |
Gratianus_Wesley_Putra_Data_Interpolating_Convolutional_Neural_ECCV_2018_paper | Interpolating Convolutional Neural Networks Using Batch Normalization | [
"Gratianus Wesley Putra Data",
"Kirjon Ngu",
"David William Murray",
"Victor Adrian Prisacariu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Gratianus_Wesley_Putra_Data_Interpolating_Convolutional_Neural_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Gratianus_Wesley_Putra_Data_Interpolating_Convolutional_Neural_ECCV_2018_paper.pdf | null | null | null | null | Perceiving a visual concept as a mixture of learned ones is natural for humans, aiding them to grasp new concepts and strengthening old ones. For all their power and recent success, deep convolutional networks do not have this ability. Inspired by recent work on universal representations for neural networks, we propose... |
Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper | Learning Blind Video Temporal Consistency | [
"Wei-Sheng Lai",
"Jia-Bin Huang",
"Oliver Wang",
"Eli Shechtman",
"Ersin Yumer",
"Ming-Hsuan Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper.pdf | null | null | 1808.00449 | title_snapshot | Applying image processing algorithms independently to each frame of a video often leads to undesired inconsistent results over time. Developing temporally consistent video-based extensions, however, requires domain knowledge for individual tasks and is unable to generalize to other applications. In this paper, we prese... |
Huajie_Jiang_Learning_Class_Prototypes_ECCV_2018_paper | Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition | [
"Huajie Jiang",
"Ruiping Wang",
"Shiguang Shan",
"Xilin Chen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Huajie_Jiang_Learning_Class_Prototypes_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Huajie_Jiang_Learning_Class_Prototypes_ECCV_2018_paper.pdf | null | null | 1807.09123 | title_snapshot | Zero-shot learning (ZSL) aims to recognize objects of novel classes without any training samples of specific classes, which is achieved by exploiting the semantic information and auxiliary datasets. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the auxiliary datase... |
Chen_Zhu_Fine-grained_Video_Categorization_ECCV_2018_paper | Fine-grained Video Categorization with Redundancy Reduction Attention | [
"Chen Zhu",
"Xiao Tan",
"Feng Zhou",
"Xiao Liu",
"Kaiyu Yue",
"Errui Ding",
"Yi Ma"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chen_Zhu_Fine-grained_Video_Categorization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chen_Zhu_Fine-grained_Video_Categorization_ECCV_2018_paper.pdf | null | null | 1810.11189 | title_snapshot | For fine-grained categorization tasks, videos could serve as a better source than static images as videos have a higher chance of containing discriminative patterns. Nevertheless, a video sequence could also contain a lot of redundant and irrelevant frames. How to locate critical information of interest is a challengin... |
Gedas_Bertasius_Object_Detection_in_ECCV_2018_paper | Object Detection in Video with Spatiotemporal Sampling Networks | [
"Gedas Bertasius",
"Lorenzo Torresani",
"Jianbo Shi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Gedas_Bertasius_Object_Detection_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Gedas_Bertasius_Object_Detection_in_ECCV_2018_paper.pdf | null | null | 1803.05549 | title_snapshot | We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in a video frame by learning to spatially sample features from the adjacent frames. This naturally renders the approach robust to occlusion or motion blur ... |
Zelun_Luo_Graph_Distillation_for_ECCV_2018_paper | Graph Distillation for Action Detection with Privileged Modalities | [
"Zelun Luo",
"Jun-Ting Hsieh",
"Lu Jiang",
"Juan Carlos Niebles",
"Li Fei-Fei"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zelun_Luo_Graph_Distillation_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zelun_Luo_Graph_Distillation_for_ECCV_2018_paper.pdf | null | null | 1712.00108 | title_snapshot | We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source... |
Po-Yu_Huang_Efficient_Uncertainty_Estimation_ECCV_2018_paper | Efficient Uncertainty Estimation for Semantic Segmentation in Videos | [
"Po-Yu Huang",
"Wan-Ting Hsu",
"Chun-Yueh Chiu",
"Ting-Fan Wu",
"Min Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Po-Yu_Huang_Efficient_Uncertainty_Estimation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Po-Yu_Huang_Efficient_Uncertainty_Estimation_ECCV_2018_paper.pdf | null | null | 1807.11037 | title_snapshot | Uncertainty estimation in deep learning becomes more important recently. A deep learning model can't be applied in real applications if we don't know whether the model is certain about the decision or not. Some literature proposes the Bayesian neural network which can estimate the uncertainty by Monte Carlo Dropout (MC... |
Shivanthan_Yohanandan_Saliency_Preservation_in_ECCV_2018_paper | Saliency Preservation in Low-Resolution Grayscale Images | [
"Shivanthan Yohanandan",
"Andy Song",
"Adrian G. Dyer",
"Dacheng Tao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shivanthan_Yohanandan_Saliency_Preservation_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shivanthan_Yohanandan_Saliency_Preservation_in_ECCV_2018_paper.pdf | null | null | 1712.02048 | title_snapshot | Visual salience detection originated over 500 million years ago and is one of nature's most efficient mechanisms. In contrast, many state-of-the-art computational saliency models are complex and inefficient. Most saliency models process high-resolution color (HC) images; however, insights into the evolutionary origins ... |
Lixiong_Chen_Polarimetric_Three-View_Geometry_ECCV_2018_paper | Polarimetric Three-View Geometry | [
"Lixiong Chen",
"Yinqiang Zheng",
"Art Subpa-asa",
"Imari Sato"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lixiong_Chen_Polarimetric_Three-View_Geometry_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lixiong_Chen_Polarimetric_Three-View_Geometry_ECCV_2018_paper.pdf | null | null | null | null | This paper theorizes the connection between polarization and three-view geometry. It presents a ubiquitous polarization-induced constraint that regulates the relative pose of a system of three cameras. We demonstrate that, in a multi-view system, the polarization phase obtained for a surface point is induced from one o... |
Nikolaos_Sarafianos_Deep_Imbalanced_Attribute_ECCV_2018_paper | Deep Imbalanced Attribute Classification using Visual Attention Aggregation | [
"Nikolaos Sarafianos",
"Xiang Xu",
"Ioannis A. Kakadiaris"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Nikolaos_Sarafianos_Deep_Imbalanced_Attribute_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Nikolaos_Sarafianos_Deep_Imbalanced_Attribute_ECCV_2018_paper.pdf | null | null | 1807.03903 | title_snapshot | For many computer vision applications, such as image description and human identification recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods ... |
Pengfei_Zhang_Adding_Attentiveness_to_ECCV_2018_paper | Adding Attentiveness to the Neurons in Recurrent Neural Networks | [
"Pengfei Zhang",
"Jianru Xue",
"Cuiling Lan",
"Wenjun Zeng",
"Zhanning Gao",
"Nanning Zheng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Pengfei_Zhang_Adding_Attentiveness_to_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Pengfei_Zhang_Adding_Attentiveness_to_ECCV_2018_paper.pdf | null | null | 1807.04445 | title_snapshot | Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information. However, the structures of existing RNN neurons mainly focus on controlling the contributions of current and historical information but do not explore the different importance levels of different elements i... |
Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper | Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal | [
"Jie Yang",
"Dong Gong",
"Lingqiao Liu",
"Qinfeng Shi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.pdf | null | null | null | null | Reflections often obstruct the desired scene when taking photos through glass panels. Removing unwanted reflection automatically from the photos is highly desirable. Traditional methods often impose certain priors or assumptions to target particular type(s) of reflection such as shifted double reflection, thus have dif... |
Michal_Polic_Fast_and_Precise_ECCV_2018_paper | Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction | [
"Michal Polic",
"Wolfgang Forstner",
"Tomas Pajdla"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Michal_Polic_Fast_and_Precise_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Michal_Polic_Fast_and_Precise_ECCV_2018_paper.pdf | null | null | 1808.02414 | title_snapshot | Estimating uncertainty of camera parameters computed in Structure from Motion (SfM) is an important tool for evaluating the quality of the reconstruction and guiding the reconstruction process. Yet, the quality of the estimated parameters of large reconstructions has been rarely evaluated due to the computational chall... |
Edgar_Margffoy-Tuay_Dynamic_Multimodal_Instance_ECCV_2018_paper | Dynamic Multimodal Instance Segmentation Guided by Natural Language Queries | [
"Edgar Margffoy-Tuay",
"Juan C. Perez",
"Emilio Botero",
"Pablo Arbelaez"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Edgar_Margffoy-Tuay_Dynamic_Multimodal_Instance_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Edgar_Margffoy-Tuay_Dynamic_Multimodal_Instance_ECCV_2018_paper.pdf | null | null | 1807.02257 | title_snapshot | We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either ( extit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by ( extit{ii}) mapping the expr... |
Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper | Learning SO(3) Equivariant Representations with Spherical CNNs | [
"Carlos Esteves",
"Christine Allen-Blanchette",
"Ameesh Makadia",
"Kostas Daniilidis"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper.pdf | null | null | 1711.06721 | title_snapshot | We address the problem of 3D rotation equivariance in convolutional neural networks. 3D rotations have been a challenging nuisance in 3D classification tasks requiring higher capacity and extended data augmentation in order to tackle it. We model 3D data with multi-valued spherical functions and we propose a novel sphe... |
Apoorv_Vyas_Out-of-Distribution_Detection_Using_ECCV_2018_paper | Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers | [
"Apoorv Vyas",
"Nataraj Jammalamadaka",
"Xia Zhu",
"Dipankar Das",
"Bharat Kaul",
"Theodore L. Willke"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Apoorv_Vyas_Out-of-Distribution_Detection_Using_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Apoorv_Vyas_Out-of-Distribution_Detection_Using_ECCV_2018_paper.pdf | null | null | 1809.03576 | title_snapshot | As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensembl... |
Yang_Du_Interaction-aware_Spatio-temporal_Pyramid_ECCV_2018_paper | Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification | [
"Yang Du",
"Chunfeng Yuan",
"Bing Li",
"Lili Zhao",
"Yangxi Li",
"Weiming Hu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yang_Du_Interaction-aware_Spatio-temporal_Pyramid_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yang_Du_Interaction-aware_Spatio-temporal_Pyramid_ECCV_2018_paper.pdf | null | null | 1808.01106 | title_snapshot | Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local feature to obtain its weight score, which ignores interactions among local featu... |
Chuanxia_Zheng_T2Net_Synthetic-to-Realistic_Translation_ECCV_2018_paper | T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks | [
"Chuanxia Zheng",
"Tat-Jen Cham",
"Jianfei Cai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chuanxia_Zheng_T2Net_Synthetic-to-Realistic_Translation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chuanxia_Zheng_T2Net_Synthetic-to-Realistic_Translation_ECCV_2018_paper.pdf | null | null | 1808.01454 | title_snapshot | Current methods for single-image depth estimation use training datasets with real image-depth pairs or stereo pairs, which are not easy to acquire. We propose a framework, trained on synthetic image-depth pairs and unpaired real images, that comprises an image translation network for enhancing realism of input images, ... |
Fanyi_Xiao_Object_Detection_with_ECCV_2018_paper | Video Object Detection with an Aligned Spatial-Temporal Memory | [
"Fanyi Xiao",
"Yong Jae Lee"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Fanyi_Xiao_Object_Detection_with_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Fanyi_Xiao_Object_Detection_with_ECCV_2018_paper.pdf | null | null | 1712.06317 | title_snapshot | We introduce Spatial-Temporal Memory Networks for video object detection. At its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent computation unit to model long-term temporal appearance and motion dynamics. The STMM's design enables full integration of pretrained backbone CNN weights, which w... |
Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper | CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering | [
"Zhengqi Li",
"Noah Snavely"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.pdf | null | null | 1808.08601 | title_snapshot | Intrinsic image decomposition is a long-standing, highly challenging computer vision problem, where ground truth data is very difficult to acquire. We explore the idea of using synthetic data to train CNN-based intrinsic image decomposition models, and applying these learned models to real-world images. To that end, we... |
Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper | Partial Adversarial Domain Adaptation | [
"Zhangjie Cao",
"Lijia Ma",
"Mingsheng Long",
"Jianmin Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.pdf | null | null | 1808.04205 | title_snapshot | Domain adversarial learning aligns the feature distributions across the source and target domains in a two-player minimax game. Existing domain adversarial networks generally assume identical label space across different domains. In the presence of big data, there is strong motivation of transferring deep models from e... |
Moitreya_Chatterjee_Diverse_and_Coherent_ECCV_2018_paper | Diverse and Coherent Paragraph Generation from Images | [
"Moitreya Chatterjee",
"Alexander G. Schwing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Moitreya_Chatterjee_Diverse_and_Coherent_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Moitreya_Chatterjee_Diverse_and_Coherent_ECCV_2018_paper.pdf | null | null | 1809.00681 | title_snapshot | Paragraph generation from images, which has gained popularity recently, is an important task for video summarization, editing, and support of the disabled. Traditional image captioning methods fall short on this front, since they aren't designed to generate long informative descriptions. Moreover, the vanilla approach ... |
Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper | Diverse Image-to-Image Translation via Disentangled Representations | [
"Hsin-Ying Lee",
"Hung-Yu Tseng",
"Jia-Bin Huang",
"Maneesh Singh",
"Ming-Hsuan Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.pdf | null | null | 1808.00948 | title_snapshot | Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for producing ... |
Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper | BOP: Benchmark for 6D Object Pose Estimation | [
"Tomas Hodan",
"Frank Michel",
"Eric Brachmann",
"Wadim Kehl",
"Anders GlentBuch",
"Dirk Kraft",
"Bertram Drost",
"Joel Vidal",
"Stephan Ihrke",
"Xenophon Zabulis",
"Caner Sahin",
"Fabian Manhardt",
"Federico Tombari",
"Tae-Kyun Kim",
"Jiri Matas",
"Carsten Rother"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper.pdf | null | null | 1808.08319 | title_snapshot | We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including... |
Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper | Generative Domain-Migration Hashing for Sketch-to-Image Retrieval | [
"Jingyi Zhang",
"Fumin Shen",
"Li Liu",
"Fan Zhu",
"Mengyang Yu",
"Ling Shao",
"Heng Tao Shen",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper.pdf | null | null | null | null | Due to the succinct nature of free-hand sketch drawings, sketch-based image retrieval (SBIR) has abundant practical use cases in consumer electronics. However, SBIR remains a long-standing unsolved problem mainly due to the significant discrepancy between the sketch domain and the image domain. In this work, we propose... |
Armand_Zampieri_Multimodal_image_alignment_ECCV_2018_paper | Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing | [
"Armand Zampieri",
"Guillaume Charpiat",
"Nicolas Girard",
"Yuliya Tarabalka"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Armand_Zampieri_Multimodal_image_alignment_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Armand_Zampieri_Multimodal_image_alignment_ECCV_2018_paper.pdf | null | null | 1802.09816 | title_judge | We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significa... |
Chen_Liu_FloorNet_A_Unified_ECCV_2018_paper | FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans | [
"Chen Liu",
"Jiaye Wu",
"Yasutaka Furukawa"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chen_Liu_FloorNet_A_Unified_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chen_Liu_FloorNet_A_Unified_ECCV_2018_paper.pdf | null | null | 1804.00090 | title_snapshot | The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket. This paper tackles this problem by proposing FloorNet, a novel deep neural architecture. The challenge lies in the processing of RGBD streams spanning a large 3D... |
SouYoung_Jin_Unsupervised_Hard-Negative_Mining_ECCV_2018_paper | Unsupervised Hard Example Mining from Videos for Improved Object Detection | [
"SouYoung Jin",
"Aruni RoyChowdhury",
"Huaizu Jiang",
"Ashish Singh",
"Aditya Prasad",
"Deep Chakraborty",
"Erik Learned-Miller"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/SouYoung_Jin_Unsupervised_Hard-Negative_Mining_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/SouYoung_Jin_Unsupervised_Hard-Negative_Mining_ECCV_2018_paper.pdf | null | null | 1808.04285 | title_snapshot | Important gains have recently been obtained in object detection by using training objectives that focus on {em hard negative} examples, i.e., negative examples that are currently rated as positive or ambiguous by the detector. These examples can strongly influence parameters when the network is trained to correct them.... |
Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper | A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment | [
"Roberto Valle",
"Jose M. Buenaposada",
"Antonio Valdes",
"Luis Baumela"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.pdf | null | null | null | null | In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT). We use a simple Convolutional Neural Network (CNN) to generate probability maps of landmarks location. These are further refined with the ERT regressor, which is initialized by fitt... |
yaxing_wang_Transferring_GANs_generating_ECCV_2018_paper | Transferring GANs: generating images from limited data | [
"Yaxing Wang",
"Chenshen Wu",
"Luis Herranz",
"Joost van de Weijer",
"Abel Gonzalez-Garcia",
"Bogdan Raducanu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/yaxing_wang_Transferring_GANs_generating_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/yaxing_wang_Transferring_GANs_generating_ECCV_2018_paper.pdf | null | null | 1805.01677 | title_snapshot | Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied... |
Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper | Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking | [
"Chenglong Li",
"Chengli Zhu",
"Yan Huang",
"Jin Tang",
"Liang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.pdf | null | null | null | null | Due to the complementary benefits of visible (RGB) and thermal infrared (T) data, RGB-T object tracking attracts more and more attention recently for boosting the performance under adverse illumination conditions. Existing RGB-T tracking methods usually localize a target object with a bounding box, in which the tracker... |
Simyung_Chang_Broadcasting_Convolutional_Network_ECCV_2018_paper | Broadcasting Convolutional Network for Visual Relational Reasoning | [
"Simyung Chang",
"John Yang",
"SeongUk Park",
"Nojun Kwak"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Simyung_Chang_Broadcasting_Convolutional_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Simyung_Chang_Broadcasting_Convolutional_Network_ECCV_2018_paper.pdf | null | null | 1712.02517 | title_snapshot | In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcas... |
Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper | DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency | [
"Yuliang Zou",
"Zelun Luo",
"Jia-Bin Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper.pdf | null | null | 1809.01649 | title_snapshot | We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propo... |
Hossam_Isack_K-convexity_shape_priors_ECCV_2018_paper | K-convexity shape priors for segmentation | [
"Hossam Isack",
"Lena Gorelick",
"Karin Ng",
"Olga Veksler",
"Yuri Boykov"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hossam_Isack_K-convexity_shape_priors_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hossam_Isack_K-convexity_shape_priors_ECCV_2018_paper.pdf | null | null | null | null | This work extends popular star-convexity and other more general forms of convexity priors. We represent an object as a union of "convex'' overlappable subsets. Since an arbitrary shape can always be divided into convex parts, our regularization model restricts the number of such parts. Previous k-part shape priors are ... |
Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper | Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data | [
"Yabin Zhang",
"Hui Tang",
"Kui Jia"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf | null | null | 1807.10916 | title_snapshot | Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pre-training the models using a rich set of auxili... |
Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper | Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition | [
"Chaojian Yu",
"Xinyi Zhao",
"Qi Zheng",
"Peng Zhang",
"Xinge You"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.pdf | null | null | 1807.09915 | title_snapshot | Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature in... |
Jiuxiang_Gu_Unpaired_Image_Captioning_ECCV_2018_paper | Unpaired Image Captioning by Language Pivoting | [
"Jiuxiang Gu",
"Shafiq Joty",
"Jianfei Cai",
"Gang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiuxiang_Gu_Unpaired_Image_Captioning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiuxiang_Gu_Unpaired_Image_Captioning_ECCV_2018_paper.pdf | null | null | 1803.05526 | title_snapshot | Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description. In general, the mapping function is learned from a training set of image-caption pairs. However, for some language, large scale image-... |
Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper | Face De-Spoofing: Anti-Spoofing via Noise Modeling | [
"Amin Jourabloo",
"Yaojie Liu",
"Xiaoming Liu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.pdf | null | null | 1807.09968 | title_snapshot | Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an indivisible unit, and process it holistically, without explicit modeling of the spoofing process. In this work, motivated by the noise mode... |
Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper | Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation | [
"Helge Rhodin",
"Mathieu Salzmann",
"Pascal Fua"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.pdf | null | null | 1804.01110 | title_snapshot | Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a sufficiently large set of samples with 3D annotations for learning ... |
Weidi_Xie_Comparator_Networks_ECCV_2018_paper | Comparator Networks | [
"Weidi Xie",
"Li Shen",
"Andrew Zisserman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Weidi_Xie_Comparator_Networks_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Weidi_Xie_Comparator_Networks_ECCV_2018_paper.pdf | null | null | 1807.11440 | title_snapshot | The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity bet... |
Xuanyu_Zhu_Quaternion_Convolutional_Neural_ECCV_2018_paper | Quaternion Convolutional Neural Networks | [
"Xuanyu Zhu",
"Yi Xu",
"Hongteng Xu",
"Changjian Chen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xuanyu_Zhu_Quaternion_Convolutional_Neural_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xuanyu_Zhu_Quaternion_Convolutional_Neural_ECCV_2018_paper.pdf | null | null | 1903.00658 | title_snapshot | Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years. However, the extensions of the neural network models in other number fields and their potential applications are not fully-investigated yet. Focusing on color images, which can be n... |
Ian_Cherabier_Learning_Priors_for_ECCV_2018_paper | Learning Priors for Semantic 3D Reconstruction | [
"Ian Cherabier",
"Johannes L. Schonberger",
"Martin R. Oswald",
"Marc Pollefeys",
"Andreas Geiger"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ian_Cherabier_Learning_Priors_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ian_Cherabier_Learning_Priors_for_ECCV_2018_paper.pdf | null | null | null | null | We present a novel semantic 3D reconstruction framework which embeds variational regularization into a neural network. Our network performs a fixed number of unrolled multi-scale optimization iterations with shared interaction weights. In contrast to existing variational methods for semantic 3D reconstruction, our mode... |
Qixing_Huang_Joint_Map_and_ECCV_2018_paper | Joint Map and Symmetry Synchronization | [
"Yifan Sun",
"Zhenxiao Liang",
"Xiangru Huang",
"Qixing Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qixing_Huang_Joint_Map_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qixing_Huang_Joint_Map_and_ECCV_2018_paper.pdf | null | null | null | null | Most existing techniques in map computation (e.g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique. This assumption, however, easily breaks when visual objects possess self-symmetries. In this paper, we study the problem of jointly optimizing self-symmetr... |
Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper | Start, Follow, Read: End-to-End Full-Page Handwriting Recognition | [
"Curtis Wigington",
"Chris Tensmeyer",
"Brian Davis",
"William Barrett",
"Brian Price",
"Scott Cohen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper.pdf | null | null | null | null | Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. HWR models are often limited by the accuracy of the preceding steps of text detection and segme... |
Shuhan_Chen_Reverse_Attention_for_ECCV_2018_paper | Reverse Attention for Salient Object Detection | [
"Shuhan Chen",
"Xiuli Tan",
"Ben Wang",
"Xuelong Hu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shuhan_Chen_Reverse_Attention_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shuhan_Chen_Reverse_Attention_for_ECCV_2018_paper.pdf | null | null | 1807.09940 | title_snapshot | Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents... |
Shangbang_Long_TextSnake_A_Flexible_ECCV_2018_paper | TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes | [
"Shangbang Long",
"Jiaqiang Ruan",
"Wenjie Zhang",
"Xin He",
"Wenhao Wu",
"Cong Yao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shangbang_Long_TextSnake_A_Flexible_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shangbang_Long_TextSnake_A_Flexible_ECCV_2018_paper.pdf | null | null | 1807.01544 | title_snapshot | Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) ad... |
Chang_Liu_Linear_Span_Network_ECCV_2018_paper | Linear Span Network for Object Skeleton Detection | [
"Chang Liu",
"Wei Ke",
"Fei Qin",
"Qixiang Ye"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chang_Liu_Linear_Span_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chang_Liu_Linear_Span_Network_ECCV_2018_paper.pdf | null | null | 1807.09601 | title_snapshot | Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies. In this paper, we first re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this, we formalize a Linea... |
Zihang_Meng_Efficient_Relative_Attribute_ECCV_2018_paper | Efficient Relative Attribute Learning using Graph Neural Networks | [
"Zihang Meng",
"Nagesh Adluru",
"Hyunwoo J. Kim",
"Glenn Fung",
"Vikas Singh"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zihang_Meng_Efficient_Relative_Attribute_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zihang_Meng_Efficient_Relative_Attribute_ECCV_2018_paper.pdf | null | null | null | null | A sizable body of work on relative attributes provides compelling evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields significant improvements in a wide variety of tasks in vision. In this paper, we show how emerging ideas in graph neural networks can yield a uni... |
Thomas_Probst_Model-free_Consensus_Maximization_ECCV_2018_paper | Model-free Consensus Maximization for Non-Rigid Shapes | [
"Thomas Probst",
"Ajad Chhatkuli",
"Danda Pani Paudel",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Thomas_Probst_Model-free_Consensus_Maximization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Thomas_Probst_Model-free_Consensus_Maximization_ECCV_2018_paper.pdf | null | null | 1807.01963 | title_snapshot | Many computer vision methods use consensus maximization to re- late measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measure... |
Archan_Ray_U-PC_Unsupervised_Planogram_ECCV_2018_paper | U-PC: Unsupervised Planogram Compliance | [
"Archan Ray",
"Nishant Kumar",
"Avishek Shaw",
"Dipti Prasad Mukherjee"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Archan_Ray_U-PC_Unsupervised_Planogram_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Archan_Ray_U-PC_Unsupervised_Planogram_ECCV_2018_paper.pdf | null | null | null | null | We present an end-to-end solution for recognizing merchandise displayed in the shelves of a supermarket. Given images of individual products, which are taken under ideal illumination for product marketing, the challenge is to find these products automatically in the images of the shelves. Note that the images of shelve... |
Pauline_Luc_Predicting_Future_Instance_ECCV_2018_paper | Predicting Future Instance Segmentation by Forecasting Convolutional Features | [
"Pauline Luc",
"Camille Couprie",
"Yann LeCun",
"Jakob Verbeek"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Pauline_Luc_Predicting_Future_Instance_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Pauline_Luc_Predicting_Future_Instance_ECCV_2018_paper.pdf | null | null | 1803.11496 | title_snapshot | Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and th... |
Xu_Lan_Person_Search_by_ECCV_2018_paper | Person Search by Multi-Scale Matching | [
"Xu Lan",
"Xiatian Zhu",
"Shaogang Gong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xu_Lan_Person_Search_by_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xu_Lan_Person_Search_by_ECCV_2018_paper.pdf | null | null | 1807.08582 | title_snapshot | We consider the problem of person search in unconstrained scene images. Existing methods usually focus on improving the person detection accuracy to mitigate negative effects imposed by misalignment, mis-detections, and false alarms resulted from noisy people auto-detection. In contrast to previous studies, we show tha... |
Yijun_Li_Flow-Grounded_Spatial-Temporal_Video_ECCV_2018_paper | Flow-Grounded Spatial-Temporal Video Prediction from Still Images | [
"Yijun Li",
"Chen Fang",
"Jimei Yang",
"Zhaowen Wang",
"Xin Lu",
"Ming-Hsuan Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yijun_Li_Flow-Grounded_Spatial-Temporal_Video_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yijun_Li_Flow-Grounded_Spatial-Temporal_Video_ECCV_2018_paper.pdf | null | null | 1807.09755 | title_snapshot | Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating consecutive multiple future frames by observing one single still image only. We formulate the multi-frame prediction task as a multiple time s... |
Tz-Ying_Wu_Liquid_Pouring_Monitoring_ECCV_2018_paper | Liquid Pouring Monitoring via Rich Sensory Inputs | [
"Tz-Ying Wu",
"Juan-Ting Lin",
"Tsun-Hsuang Wang",
"Chan-Wei Hu",
"Juan Carlos Niebles",
"Min Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tz-Ying_Wu_Liquid_Pouring_Monitoring_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tz-Ying_Wu_Liquid_Pouring_Monitoring_ECCV_2018_paper.pdf | null | null | 1808.01725 | title_snapshot | Humans have the amazing ability to perform very subtle manipulation task using a closed-loop control system with imprecise mechanics (i.e., our body parts) but rich sensory information (e.g., vision, tactile, etc.). In the closed-loop system, the ability to monitor the state of the task via rich sensory information is ... |
Mir_Rayat_Imtiaz_Hossain_Exploiting_temporal_information_ECCV_2018_paper | Exploiting temporal information for 3D human pose estimation | [
"Mir Rayat Imtiaz Hossain",
"James J. Little"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Mir_Rayat_Imtiaz_Hossain_Exploiting_temporal_information_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Mir_Rayat_Imtiaz_Hossain_Exploiting_temporal_information_ECCV_2018_paper.pdf | null | null | null | null | In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the ef... |
Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper | Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization | [
"Kuang-Jui Hsu",
"Chung-Chi Tsai",
"Yen-Yu Lin",
"Xiaoning Qian",
"Yung-Yu Chuang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN). Our method does not require any additional training data in the form of object masks. We decompose co-saliency detection into two sub-tasks, single-image... |
Kemal_Oksuz_Localization_Recall_Precision_ECCV_2018_paper | Localization Recall Precision (LRP): A New Performance Metric for Object Detection | [
"Kemal Oksuz",
"Baris Can Cam",
"Emre Akbas",
"Sinan Kalkan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kemal_Oksuz_Localization_Recall_Precision_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kemal_Oksuz_Localization_Recall_Precision_ECCV_2018_paper.pdf | null | null | 1807.01696 | title_snapshot | Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuri... |
Paul_Hongsuck_Seo_Attentive_Semantic_Alignment_ECCV_2018_paper | Attentive Semantic Alignment with Offset-Aware Correlation Kernels | [
"Paul Hongsuck Seo",
"Jongmin Lee",
"Deunsol Jung",
"Bohyung Han",
"Minsu Cho"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Paul_Hongsuck_Seo_Attentive_Semantic_Alignment_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Paul_Hongsuck_Seo_Attentive_Semantic_Alignment_ECCV_2018_paper.pdf | null | null | 1808.02128 | title_snapshot | Semantic correspondence is the problem of establishing correspondences across images depicting different instances of the same object or scene class. One of recent approaches to this problem is to estimate parameters of a global transformation model that densely aligns one image to the other. Since an entire correlatio... |
Rishabh_Dabral_Learning_3D_Human_ECCV_2018_paper | Learning 3D Human Pose from Structure and Motion | [
"Rishabh Dabral",
"Anurag Mundhada",
"Uday Kusupati",
"Safeer Afaque",
"Abhishek Sharma",
"Arjun Jain"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Rishabh_Dabral_Learning_3D_Human_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Rishabh_Dabral_Learning_3D_Human_ECCV_2018_paper.pdf | null | null | 1711.09250 | title_snapshot | 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthe... |
Qiang_Qiu_ForestHash_Semantic_Hashing_ECCV_2018_paper | ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks | [
"Qiang Qiu",
"Jose Lezama",
"Alex Bronstein",
"Guillermo Sapiro"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qiang_Qiu_ForestHash_Semantic_Hashing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qiang_Qiu_ForestHash_Semantic_Hashing_ECCV_2018_paper.pdf | null | null | 1711.08364 | title_snapshot | In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests. A binary hash code for a data point is obtained by a set of decision trees, setting `1' for the visited tree leaf, and `0' for the rest. We propose to first randomly grou... |
Zheng_Shou_Online_Detection_of_ECCV_2018_paper | Online Detection of Action Start in Untrimmed, Streaming Videos | [
"Zheng Shou",
"Junting Pan",
"Jonathan Chan",
"Kazuyuki Miyazawa",
"Hassan Mansour",
"Anthony Vetro",
"Xavier Giro-i-Nieto",
"Shih-Fu Chang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zheng_Shou_Online_Detection_of_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zheng_Shou_Online_Detection_of_ECCV_2018_paper.pdf | null | null | 1802.06822 | title_snapshot | We aim to tackle a novel task in action detection - Online Detection of Action Start (ODAS) in untrimmed, streaming videos. The goal of ODAS is to detect the start of an action instance, with high categorization accuracy and low detection latency. ODAS is important in many applications such as early alert generation to... |
Dhruv_Mahajan_Exploring_the_Limits_ECCV_2018_paper | Exploring the Limits of Weakly Supervised Pretraining | [
"Dhruv Mahajan",
"Ross Girshick",
"Vignesh Ramanathan",
"Kaiming He",
"Manohar Paluri",
"Yixuan Li",
"Ashwin Bharambe",
"Laurens van der Maaten"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dhruv_Mahajan_Exploring_the_Limits_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dhruv_Mahajan_Exploring_the_Limits_ECCV_2018_paper.pdf | null | null | 1805.00932 | title_snapshot | State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet, ImageNet is now nearly ten years old and is by modern standards "small". Even so, relatively little is known about the behavior of pretraini... |
Bowen_Cheng_Revisiting_RCNN_On_ECCV_2018_paper | Revisiting RCNN: On Awakening the Classification Power of Faster RCNN | [
"Bowen Cheng",
"Yunchao Wei",
"Honghui Shi",
"Rogerio Feris",
"Jinjun Xiong",
"Thomas Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Bowen_Cheng_Revisiting_RCNN_On_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Bowen_Cheng_Revisiting_RCNN_On_ECCV_2018_paper.pdf | null | null | 1803.06799 | title_snapshot | Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization.... |
Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper | HandMap: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision | [
"Xiaokun Wu",
"Daniel Finnegan",
"Eamonn O'Neill",
"Yong-Liang Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.pdf | null | null | null | null | This work presents a novel hand pose estimation framework via intermediate dense guidance map supervision. By leveraging the advantage of predicting heat maps of hand joints in detection-based methods, we propose to use dense feature maps through intermediate supervision in a regression-based framework that is not limi... |
Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper | Unsupervised Learning of Multi-Frame Optical Flow with Occlusions | [
"Joel Janai",
"Fatma Guney",
"Anurag Ranjan",
"Michael Black",
"Andreas Geiger"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper.pdf | null | null | null | null | Learning optical flow with neural networks is hampered by the need for obtaining training data with associated ground truth. Unsupervised learning is a promising direction, yet the performance of current unsupervised methods is still limited. In particular, the lack of proper occlusion handling in commonly used data te... |
Shervin_Ardeshir_Integrating_Egocentric_Videos_ECCV_2018_paper | Integrating Egocentric Videos in Top-view Surveillance Videos: Joint Identification and Temporal Alignment | [
"Shervin Ardeshir",
"Ali Borji"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shervin_Ardeshir_Integrating_Egocentric_Videos_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shervin_Ardeshir_Integrating_Egocentric_Videos_ECCV_2018_paper.pdf | null | null | null | null | Videos recorded from first person (egocentric) perspective have little visual appearance in common with those from third person perspective, especially with videos captured by top-view surveillance cameras. In this paper, we aim to relate these two sources of information from a surveillance standpoint, namely in terms ... |
Yongyi_Lu_Attribute-Guided_Face_Generation_ECCV_2018_paper | Attribute-Guided Face Generation Using Conditional CycleGAN | [
"Yongyi Lu",
"Yu-Wing Tai",
"Chi-Keung Tang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yongyi_Lu_Attribute-Guided_Face_Generation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yongyi_Lu_Attribute-Guided_Face_Generation_ECCV_2018_paper.pdf | null | null | 1705.09966 | title_snapshot | We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the ... |
Keisuke_Tateno_Distortion-Aware_Convolutional_Filters_ECCV_2018_paper | Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images | [
"Keisuke Tateno",
"Nassir Navab",
"Federico Tombari"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Keisuke_Tateno_Distortion-Aware_Convolutional_Filters_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Keisuke_Tateno_Distortion-Aware_Convolutional_Filters_ECCV_2018_paper.pdf | null | null | null | null | There is a high demand of 3D data for 360° panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e.g., omnidirectional cameras) as well as visualizing in 3D (e.g., head mounted displays) panoramic images and videos. At the same time, 3D sensors able to... |
Ying_Fu_Joint_Camera_Spectral_ECCV_2018_paper | Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery | [
"Ying Fu",
"Tao Zhang",
"Yinqiang Zheng",
"Debing Zhang",
"Hua Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ying_Fu_Joint_Camera_Spectral_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ying_Fu_Joint_Camera_Spectral_ECCV_2018_paper.pdf | null | null | null | null | Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been shown to be sensitive to the camera spectral sensitivity (CSS). In this paper, we present an efficient convolutional neural network (CNN) based method, which can jointly select the optimal CSS fr... |
Minhyeok_Heo_Monocular_Depth_Estimation_ECCV_2018_paper | Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement | [
"Minhyeok Heo",
"Jaehan Lee",
"Kyung-Rae Kim",
"Han-Ul Kim",
"Chang-Su Kim"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Minhyeok_Heo_Monocular_Depth_Estimation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Minhyeok_Heo_Monocular_Depth_Estimation_ECCV_2018_paper.pdf | null | null | null | null | We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement. First, we develop a convolutional neural network (CNN) tailored for the depth estimation. Specifically, we design a novel filter, called WSM, to exploit... |
Jinlong_YANG_Analyzing_Clothing_Layer_ECCV_2018_paper | Analyzing Clothing Layer Deformation Statistics of 3D Human Motions | [
"Jinlong Yang",
"Jean-Sebastien Franco",
"Franck Hetroy-Wheeler",
"Stefanie Wuhrer"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jinlong_YANG_Analyzing_Clothing_Layer_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jinlong_YANG_Analyzing_Clothing_Layer_ECCV_2018_paper.pdf | null | null | null | null | Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for individual applicatio... |
Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper | Image Super-Resolution Using Very Deep Residual Channel Attention Networks | [
"Yulun Zhang",
"Kunpeng Li",
"Kai Li",
"Lichen Wang",
"Bineng Zhong",
"Yun Fu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper.pdf | null | null | 1807.02758 | title_snapshot | Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution (LR) inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hi... |
Guanan_Wang_Semi-Supervised_Generative_Adversarial_ECCV_2018_paper | Semi-Supervised Generative Adversarial Hashing for Image Retrieval | [
"Guan'an Wang",
"Qinghao Hu",
"Jian Cheng",
"Zengguang Hou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Guanan_Wang_Semi-Supervised_Generative_Adversarial_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Guanan_Wang_Semi-Supervised_Generative_Adversarial_ECCV_2018_paper.pdf | null | null | null | null | With explosive growth of image and video data on the Internet, hashing technique has been extensively studied for large-scale visual search. Benefiting from the advance of deep learning, deep hashing methods have achieved promising performance. However, those deep hashing models are usually trained with supervised info... |
Guandao_Yang_A_Unified_Framework_ECCV_2018_paper | Learning Single-View 3D Reconstruction with Limited Pose Supervision | [
"Guandao Yang",
"Yin Cui",
"Serge Belongie",
"Bharath Hariharan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.pdf | null | null | null | null | It is expensive to label images with 3D structure or precise camera pose. Yet, this is precisely the kind of annotation required to train single-view 3D reconstruction models. In contrast, unlabeled images or images with just category labels are easy to acquire, but few current models can use this weak supervision. We ... |
Zhengqin_Li_Materials_for_Masses_ECCV_2018_paper | Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image | [
"Zhengqin Li",
"Kalyan Sunkavalli",
"Manmohan Chandraker"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhengqin_Li_Materials_for_Masses_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhengqin_Li_Materials_for_Masses_ECCV_2018_paper.pdf | null | null | 1804.05790 | title_snapshot | We propose a material acquisition system that can recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our technique images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simu... |
Yan_Wang_Spatial_Pyramid_Calibration_ECCV_2018_paper | Multi-Scale Spatially-Asymmetric Recalibration for Image Classification | [
"Yan Wang",
"Lingxi Xie",
"Siyuan Qiao",
"Ya Zhang",
"Wenjun Zhang",
"Alan L. Yuille"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yan_Wang_Spatial_Pyramid_Calibration_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yan_Wang_Spatial_Pyramid_Calibration_ECCV_2018_paper.pdf | null | null | 1804.00787 | title_snapshot | Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to use spatial information. This paper addresses this issue by a recalibration process, which refers to the surrounding region of each neuron, computes an importance value and multiplies ... |
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