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Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper | Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation | [
"Zhengming Ding",
"Sheng Li",
"Ming Shao",
"Yun Fu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.pdf | null | null | null | null | Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better sol... |
Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper | Improving Sequential Determinantal Point Processes for Supervised Video Summarization | [
"Aidean Sharghi",
"Ali Borji",
"Chengtao Li",
"Tianbao Yang",
"Boqing Gong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper.pdf | null | null | 1807.10957 | title_snapshot | It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. Th... |
Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper | Specular-to-Diffuse Translation for Multi-View Reconstruction | [
"Shihao Wu",
"Hui Huang",
"Tiziano Portenier",
"Matan Sela",
"Daniel Cohen-Or",
"Ron Kimmel",
"Matthias Zwicker"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.pdf | null | null | 1807.05439 | title_snapshot | Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse on... |
Yingwei_Li_RESOUND_Towards_Action_ECCV_2018_paper | RESOUND: Towards Action Recognition without Representation Bias | [
"Yingwei Li",
"Yi Li",
"Nuno Vasconcelos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yingwei_Li_RESOUND_Towards_Action_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yingwei_Li_RESOUND_Towards_Action_ECCV_2018_paper.pdf | null | null | null | null | While large datasets have proven to be a key enabler for progress in computer vision, they can have biases that lead to erroneous conclusions. The notion of the representation bias of a dataset is proposed to combat this problem. It captures the fact that representations other than the ground-truth representation can a... |
Mathieu_Garon_A_Framework_for_ECCV_2018_paper | A Framework for Evaluating 6-DOF Object Trackers | [
"Mathieu Garon",
"Denis Laurendeau",
"Jean-Francois Lalonde"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Mathieu_Garon_A_Framework_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Mathieu_Garon_A_Framework_for_ECCV_2018_paper.pdf | null | null | 1803.10075 | title_snapshot | We present a challenging and realistic novel dataset for evaluating 6-DOF object tracking algorithms. Existing datasets show serious limitations---notably, unrealistic synthetic data, or real data with large fiducial markers---preventing the community from obtaining an accurate picture of the state-of-the-art. Using a ... |
Dong_Lao_Extending_Layered_Models_ECCV_2018_paper | Extending Layered Models to 3D Motion | [
"Dong Lao",
"Ganesh Sundaramoorthi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dong_Lao_Extending_Layered_Models_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dong_Lao_Extending_Layered_Models_ECCV_2018_paper.pdf | null | null | null | null | We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera. Wegeneralize layered inference to the aforementioned case and correspond-ing self-occlusion phenomena. We accomplish this by in... |
Efstratios_Gavves_Long-term_Tracking_in_ECCV_2018_paper | Long-term Tracking in the Wild: a Benchmark | [
"Jack Valmadre",
"Luca Bertinetto",
"Joao F. Henriques",
"Ran Tao",
"Andrea Vedaldi",
"Arnold W.M. Smeulders",
"Philip H.S. Torr",
"Efstratios Gavves"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Efstratios_Gavves_Long-term_Tracking_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Efstratios_Gavves_Long-term_Tracking_in_ECCV_2018_paper.pdf | null | null | 1803.09502 | title_snapshot | We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seco... |
HUSEYIN_COSKUN_Human_Motion_Analysis_ECCV_2018_paper | Human Motion Analysis with Deep Metric Learning | [
"Huseyin Coskun",
"David Joseph Tan",
"Sailesh Conjeti",
"Nassir Navab",
"Federico Tombari"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/HUSEYIN_COSKUN_Human_Motion_Analysis_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/HUSEYIN_COSKUN_Human_Motion_Analysis_ECCV_2018_paper.pdf | null | null | 1807.11176 | title_snapshot | Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail ... |
Jyh-Jing_Hwang_Adaptive_Affinity_Field_ECCV_2018_paper | Adaptive Affinity Fields for Semantic Segmentation | [
"Tsung-Wei Ke",
"Jyh-Jing Hwang",
"Ziwei Liu",
"Stella X. Yu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jyh-Jing_Hwang_Adaptive_Affinity_Field_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jyh-Jing_Hwang_Adaptive_Affinity_Field_ECCV_2018_paper.pdf | null | null | 1803.10335 | title_snapshot | Existing semantic segmentation methods mostly rely on per-pixel supervision, unable to capture structural regularity present in natural images. Instead of learning to enforce semantic labels on individual pixels, we propose to enforce affinity field patterns in individual pixel neighbourhoods, i.e., the semantic label ... |
Hyo_Jin_Kim_Hierarchy_of_Alternating_ECCV_2018_paper | Hierarchy of Alternating Specialists for Scene Recognition | [
"Hyo Jin Kim",
"Jan-Michael Frahm"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hyo_Jin_Kim_Hierarchy_of_Alternating_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hyo_Jin_Kim_Hierarchy_of_Alternating_ECCV_2018_paper.pdf | null | null | null | null | We introduce a method for improving convolutional neural networks (CNNs) for scene classification. We present a hierarchy of specialist networks, which disentangles the intra-class variation and inter-class similarity in a coarse to fine manner. Our key insight is that each subset within a class is often associated wit... |
Lipeng_Ke_Multi-Scale_Structure-Aware_Network_ECCV_2018_paper | Multi-Scale Structure-Aware Network for Human Pose Estimation | [
"Lipeng Ke",
"Ming-Ching Chang",
"Honggang Qi",
"Siwei Lyu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lipeng_Ke_Multi-Scale_Structure-Aware_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lipeng_Ke_Multi-Scale_Structure-Aware_Network_ECCV_2018_paper.pdf | null | null | 1803.09894 | title_snapshot | We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across sc... |
Sergio_Silva_License_Plate_Detection_ECCV_2018_paper | License Plate Detection and Recognition in Unconstrained Scenarios | [
"Sergio Montazzolli Silva",
"Claudio Rosito Jung"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.pdf | null | null | null | null | Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. European, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This wor... |
Panna_Felsen_Where_Will_They_ECCV_2018_paper | Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders | [
"Panna Felsen",
"Patrick Lucey",
"Sujoy Ganguly"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Panna_Felsen_Where_Will_They_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Panna_Felsen_Where_Will_They_ECCV_2018_paper.pdf | null | null | null | null | Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e.g., autonomous vehicles, security, surveillance, sports). In this paper, we present a technique using conditional variational autoencoder which learns a model that "p... |
Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper | Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States | [
"Guosheng Hu",
"Li Liu",
"Yang Yuan",
"Zehao Yu",
"Yang Hua",
"Zhihong Zhang",
"Fumin Shen",
"Ling Shao",
"Timothy Hospedales",
"Neil Robertson",
"Yongxin Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper.pdf | null | null | null | null | Facial expression recognition is a topical task. However, very little research investigates subtle expression recognition, which is important for mental activity analysis, deception detection, etc. We address subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (M... |
Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper | PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction | [
"Yifei Shi",
"Kai Xu",
"Matthias Niessner",
"Szymon Rusinkiewicz",
"Thomas Funkhouser"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper.pdf | null | null | 1803.08407 | title_snapshot | We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural net that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from ot... |
Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper | PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors | [
"Haowen Deng",
"Tolga Birdal",
"Slobodan Ilic"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper.pdf | null | null | 1808.10322 | title_snapshot | We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point... |
Yidan_Zhou_HBE_Hand_Branch_ECCV_2018_paper | HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation | [
"Yidan Zhou",
"Jian Lu",
"Kuo Du",
"Xiangbo Lin",
"Yi Sun",
"Xiaohong Ma"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yidan_Zhou_HBE_Hand_Branch_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yidan_Zhou_HBE_Hand_Branch_ECCV_2018_paper.pdf | null | null | null | null | The goal of this paper is to estimate the 3D coordinates of the hand joints from a single depth image. To give consideration to both the accuracy and the real time performance, we design a novel three-branch Convolutional Neural Networks named Hand Branch Ensemble network (HBE), where the three branches correspond to t... |
Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper | ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking | [
"Oliver Groth",
"Fabian B. Fuchs",
"Ingmar Posner",
"Andrea Vedaldi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper.pdf | null | null | 1804.08018 | title_snapshot | Physical intuition is pivotal for intelligent agents to perform complex tasks. In this paper we investigate the passive acquisition of an intuitive understanding of physical principles as well as the active utilisation of this intuition in the context of generalised object stacking. To this end, we provide ShapeStacks:... |
Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper | Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification | [
"Xing Wei",
"Yue Zhang",
"Yihong Gong",
"Jiawei Zhang",
"Nanning Zheng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.pdf | null | null | null | null | Designing discriminative and invariant features is the key to visual recognition. Recently, the bilinear pooled feature matrix of Convolutional Neural Network (CNN) has shown to achieve state-of-the-art performance on a range of fine-grained visual recognition tasks. The bilinear feature matrix collects second-order st... |
Hong-Min_Chu_Deep_Generative_Models_ECCV_2018_paper | Deep Generative Models for Weakly-Supervised Multi-Label Classification | [
"Hong-Min Chu",
"Chih-Kuan Yeh",
"Yu-Chiang Frank Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hong-Min_Chu_Deep_Generative_Models_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hong-Min_Chu_Deep_Generative_Models_ECCV_2018_paper.pdf | null | null | null | null | In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data. Since such annotation process is in general costly, we focus on the learning task of weakly-supervised multi-label classification (WS-MLC). In this paper, we tack... |
Wenqiang_Xu_SRDA_Generating_Instance_ECCV_2018_paper | SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation | [
"Wenqiang Xu",
"Yonglu Li",
"Cewu Lu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wenqiang_Xu_SRDA_Generating_Instance_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wenqiang_Xu_SRDA_Generating_Instance_ECCV_2018_paper.pdf | null | null | 1801.08839 | title_snapshot | Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quant... |
Siddharth_Tourani_MPLP_Fast_Parallel_ECCV_2018_paper | MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models | [
"Siddharth Tourani",
"Alexander Shekhovtsov",
"Carsten Rother",
"Bogdan Savchynskyy"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Siddharth_Tourani_MPLP_Fast_Parallel_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Siddharth_Tourani_MPLP_Fast_Parallel_ECCV_2018_paper.pdf | null | null | 2004.08227 | title_snapshot | Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a lo... |
Yuan-Ting_Hu_Unsupervised_Video_Object_ECCV_2018_paper | Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation | [
"Yuan-Ting Hu",
"Jia-Bin Huang",
"Alexander G. Schwing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yuan-Ting_Hu_Unsupervised_Video_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yuan-Ting_Hu_Unsupervised_Video_Object_ECCV_2018_paper.pdf | null | null | 1809.01125 | title_snapshot | Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimat... |
Yanbei_Chen_Semi-Supervised_Deep_Learning_ECCV_2018_paper | Semi-Supervised Deep Learning with Memory | [
"Yanbei Chen",
"Xiatian Zhu",
"Shaogang Gong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yanbei_Chen_Semi-Supervised_Deep_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yanbei_Chen_Semi-Supervised_Deep_Learning_ECCV_2018_paper.pdf | null | null | null | null | We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ... |
Liangliang_Ren_Deep_Reinforcement_Learning_ECCV_2018_paper | Deep Reinforcement Learning with Iterative Shift for Visual Tracking | [
"Liangliang Ren",
"Xin Yuan",
"Jiwen Lu",
"Ming Yang",
"Jie Zhou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Liangliang_Ren_Deep_Reinforcement_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Liangliang_Ren_Deep_Reinforcement_Learning_ECCV_2018_paper.pdf | null | null | null | null | Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking. In this paper, we propose a deep reinforcement learning with iterative shift (DRL-IS) method for single object tracking, wher... |
Olivia_Wiles_X2Face_A_network_ECCV_2018_paper | X2Face: A network for controlling face generation using images, audio, and pose codes | [
"Olivia Wiles",
"A. Sophia Koepke",
"Andrew Zisserman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.pdf | null | null | 1807.10550 | title_judge | The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2Face, ... |
Baosheng_Yu_Correcting_the_Triplet_ECCV_2018_paper | Correcting the Triplet Selection Bias for Triplet Loss | [
"Baosheng Yu",
"Tongliang Liu",
"Mingming Gong",
"Changxing Ding",
"Dacheng Tao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Baosheng_Yu_Correcting_the_Triplet_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Baosheng_Yu_Correcting_the_Triplet_ECCV_2018_paper.pdf | null | null | null | null | Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet mining is thus indispensable for efficientl... |
Lisa_Anne_Hendricks_Women_also_Snowboard_ECCV_2018_paper | Women also Snowboard: Overcoming Bias in Captioning Models | [
"Lisa Anne Hendricks",
"Kaylee Burns",
"Kate Saenko",
"Trevor Darrell",
"Anna Rohrbach"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lisa_Anne_Hendricks_Women_also_Snowboard_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lisa_Anne_Hendricks_Women_also_Snowboard_ECCV_2018_paper.pdf | null | null | 1803.09797 | title_snapshot | Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data (e.g., if a word is present in 60% of training sentences, it might be pr... |
Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper | GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction | [
"Li Jiang",
"Shaoshuai Shi",
"Xiaojuan Qi",
"Jiaya Jia"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single view image. Distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point l... |
Yuhang_Song_Contextual_Based_Image_ECCV_2018_paper | Contextual-based Image Inpainting: Infer, Match, and Translate | [
"Yuhang Song",
"Chao Yang",
"Zhe Lin",
"Xiaofeng Liu",
"Qin Huang",
"Hao Li",
"C.-C. Jay Kuo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yuhang_Song_Contextual_Based_Image_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yuhang_Song_Contextual_Based_Image_ECCV_2018_paper.pdf | null | null | 1711.08590 | title_snapshot | We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly ... |
Shuangjun_Liu_Inner_Space_Preserving_ECCV_2018_paper | Inner Space Preserving Generative Pose Machine | [
"Shuangjun Liu",
"Sarah Ostadabbas"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shuangjun_Liu_Inner_Space_Preserving_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shuangjun_Liu_Inner_Space_Preserving_ECCV_2018_paper.pdf | null | null | 1808.02104 | title_snapshot | Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figure... |
Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper | SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network | [
"Yancheng Bai",
"Yongqiang Zhang",
"Mingli Ding",
"Bernard Ghanem"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.pdf | null | null | null | null | Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects on large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfaction. The reason is that small objects lack sufficien... |
Xiaofeng_Liu_Dependency-aware_Attention_Control_ECCV_2018_paper | Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets | [
"Xiaofeng Liu",
"B.V.K Vijaya Kumar",
"Chao Yang",
"Qingming Tang",
"Jane You"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaofeng_Liu_Dependency-aware_Attention_Control_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaofeng_Liu_Dependency-aware_Attention_Control_ECCV_2018_paper.pdf | null | null | 1907.03030 | title_snapshot | This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or based on their inde... |
Yunlong_Wang_End-to-end_View_Synthesis_ECCV_2018_paper | End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN | [
"Yunlong Wang",
"Fei Liu",
"Zilei Wang",
"Guangqi Hou",
"Zhenan Sun",
"Tieniu Tan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yunlong_Wang_End-to-end_View_Synthesis_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yunlong_Wang_End-to-end_View_Synthesis_ECCV_2018_paper.pdf | null | null | null | null | Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. ... |
Viresh_Ranjan_Iterative_Crowd_Counting_ECCV_2018_paper | Iterative Crowd Counting | [
"Viresh Ranjan",
"Hieu Le",
"Minh Hoai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Viresh_Ranjan_Iterative_Crowd_Counting_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Viresh_Ranjan_Iterative_Crowd_Counting_ECCV_2018_paper.pdf | null | null | 1807.09959 | title_snapshot | In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resoluti... |
Weixuan_Chen_DeepPhys_Video-Based_Physiological_ECCV_2018_paper | DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks | [
"Weixuan Chen",
"Daniel McDuff"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Weixuan_Chen_DeepPhys_Video-Based_Physiological_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Weixuan_Chen_DeepPhys_Video-Based_Physiological_ECCV_2018_paper.pdf | null | null | 1805.07888 | title_snapshot | Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate u... |
Matthew_Trager_On_the_Solvability_ECCV_2018_paper | On the Solvability of Viewing Graphs | [
"Matthew Trager",
"Brian Osserman",
"Jean Ponce"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Matthew_Trager_On_the_Solvability_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Matthew_Trager_On_the_Solvability_ECCV_2018_paper.pdf | null | null | 1808.02856 | title_snapshot | A set of fundamental matrices relating pairs of cameras in some configuration can be represented as edges of a ``viewing graph''. Whether or not these fundamental matrices are generically sufficient to recover the global camera configuration depends on the structure of this graph. We study characterizations of ``solvab... |
Tianyun_Zhang_A_Systematic_DNN_ECCV_2018_paper | A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers | [
"Tianyun Zhang",
"Shaokai Ye",
"Kaiqi Zhang",
"Jian Tang",
"Wujie Wen",
"Makan Fardad",
"Yanzhi Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tianyun_Zhang_A_Systematic_DNN_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tianyun_Zhang_A_Systematic_DNN_ECCV_2018_paper.pdf | null | null | 1804.03294 | title_snapshot | Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DN... |
Kyungmin_Kim_Multimodal_Dual_Attention_ECCV_2018_paper | Multimodal Dual Attention Memory for Video Story Question Answering | [
"Kyung-Min Kim",
"Seong-Ho Choi",
"Jin-Hwa Kim",
"Byoung-Tak Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kyungmin_Kim_Multimodal_Dual_Attention_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kyungmin_Kim_Multimodal_Dual_Attention_ECCV_2018_paper.pdf | null | null | 1809.07999 | title_snapshot | We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these late... |
Kim_SAN_Learning_Relationship_ECCV_2018_paper | SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection | [
"Yonghyun Kim",
"Bong-Nam Kang",
"Daijin Kim"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kim_SAN_Learning_Relationship_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kim_SAN_Learning_Relationship_ECCV_2018_paper.pdf | null | null | 1808.04974 | title_snapshot | Most of the recent successful methods in accurate object detection build on the convolutional neural networks (CNN). However, due to the lack of scale normalization in CNN-based detection methods, the activated channels in the feature space can be completely different according to a scale and this difference makes it h... |
Lluis_Gomez_Single_Shot_Scene_ECCV_2018_paper | Single Shot Scene Text Retrieval | [
"Lluis Gomez",
"Andres Mafla",
"Marcal Rusinol",
"Dimosthenis Karatzas"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lluis_Gomez_Single_Shot_Scene_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lluis_Gomez_Single_Shot_Scene_ECCV_2018_paper.pdf | null | null | 1808.09044 | title_snapshot | Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The ... |
Michelle_Guo_Focus_on_the_ECCV_2018_paper | Dynamic Task Prioritization for Multitask Learning | [
"Michelle Guo",
"Albert Haque",
"De-An Huang",
"Serena Yeung",
"Li Fei-Fei"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Michelle_Guo_Focus_on_the_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf | null | null | null | null | We propose dynamic task prioritization for multitask learning. This allows a model to dynamically prioritize difficult tasks during training, where difficulty is inversely proportional to performance, and where difficulty changes over time. In contrast to curriculum learning, where easy tasks are prioritized above diff... |
SEUNG_HYUN_LEE_Self-supervised_Knowledge_Distillation_ECCV_2018_paper | Self-supervised Knowledge Distillation Using Singular Value Decomposition | [
"Seung Hyun Lee",
"Dae Ha Kim",
"Byung Cheol Song"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/SEUNG_HYUN_LEE_Self-supervised_Knowledge_Distillation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/SEUNG_HYUN_LEE_Self-supervised_Knowledge_Distillation_ECCV_2018_paper.pdf | null | null | 1807.06819 | title_snapshot | To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN has limited range of use, and the knowledge of T-DNN is insufficiently transferred to S-DNN. To ... |
Yu_Liu_Transductive_Centroid_Projection_ECCV_2018_paper | Transductive Centroid Projection for Semi-supervised Large-scale Recognition | [
"Yu Liu",
"Guanglu Song",
"Jing Shao",
"Xiao Jin",
"Xiaogang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yu_Liu_Transductive_Centroid_Projection_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yu_Liu_Transductive_Centroid_Projection_ECCV_2018_paper.pdf | null | null | null | null | Conventional deep semi-supervised learning methods, such as recursive clustering and training process, suffer from cumulative error and high computational complexity when collaborating with Convolutional Neural Networks. To this end, we design a simple but effective learning mechanism that merely substitutes the last f... |
Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper | Deep Shape Matching | [
"Filip Radenovic",
"Giorgos Tolias",
"Ondrej Chum"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper.pdf | null | null | 1709.03409 | title_snapshot | We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatic... |
Namhyuk_Ahn_Fast_Accurate_and_ECCV_2018_paper | Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network | [
"Namhyuk Ahn",
"Byungkon Kang",
"Kyung-Ah Sohn"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Namhyuk_Ahn_Fast_Accurate_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Namhyuk_Ahn_Fast_Accurate_and_ECCV_2018_paper.pdf | null | null | 1803.08664 | title_snapshot | In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accu... |
Xiaodan_Liang_CIRL_Controllable_Imitative_ECCV_2018_paper | CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving | [
"Xiaodan Liang",
"Tairui Wang",
"Luona Yang",
"Eric Xing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaodan_Liang_CIRL_Controllable_Imitative_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaodan_Liang_CIRL_Controllable_Imitative_ECCV_2018_paper.pdf | null | null | 1807.03776 | title_snapshot | Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy. The traditional modular pipeline heavily relies on hand-designed rules and the pre-processing perception system while the supervised learning-based models are limited by t... |
Lequan_Yu_EC-Net_an_Edge-aware_ECCV_2018_paper | EC-Net: an Edge-aware Point set Consolidation Network | [
"Lequan Yu",
"Xianzhi Li",
"Chi-Wing Fu",
"Daniel Cohen-Or",
"Pheng-Ann Heng"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lequan_Yu_EC-Net_an_Edge-aware_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lequan_Yu_EC-Net_an_Edge-aware_ECCV_2018_paper.pdf | null | null | 1807.06010 | title_snapshot | Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based {em edge-aware} technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train ... |
Lei_Chen_Part-Activated_Deep_Reinforcement_ECCV_2018_paper | Part-Activated Deep Reinforcement Learning for Action Prediction | [
"Lei Chen",
"Jiwen Lu",
"Zhanjie Song",
"Jie Zhou"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Lei_Chen_Part-Activated_Deep_Reinforcement_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Lei_Chen_Part-Activated_Deep_Reinforcement_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structu... |
Ze_Yang_Learning_to_Navigate_ECCV_2018_paper | Learning to Navigate for Fine-grained Classification | [
"Ze Yang",
"Tiange Luo",
"Dong Wang",
"Zhiqiang Hu",
"Jun Gao",
"Liwei Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ze_Yang_Learning_to_Navigate_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ze_Yang_Learning_to_Navigate_ECCV_2018_paper.pdf | null | null | 1809.00287 | title_snapshot | Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the n... |
Jie_Guo_Single_Image_Highlight_ECCV_2018_paper | Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model | [
"Jie Guo",
"Zuojian Zhou",
"Limin Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jie_Guo_Single_Image_Highlight_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jie_Guo_Single_Image_Highlight_ECCV_2018_paper.pdf | null | null | null | null | We propose a sparse and low-rank reflection model for specular highlight detection and removal using a single input image. This model is motivated by the observation that the specular highlight of a natural image usually has large intensity but is rather sparsely distributed while the remaining diffuse reflection can b... |
Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper | Improving Shape Deformation in Unsupervised Image-to-Image Translation | [
"Aaron Gokaslan",
"Vivek Ramanujan",
"Daniel Ritchie",
"Kwang In Kim",
"James Tompkin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper.pdf | null | null | 1808.04325 | title_snapshot | Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically un- successful when the domains require larger shape change. Inspired by semantic segmentation, we introduce a discriminator with dilated convo- lutions which is able to use information from acro... |
Chong_You_A_Scalable_Exemplar-based_ECCV_2018_paper | Scalable Exemplar-based Subspace Clustering on Class-Imbalanced Data | [
"Chong You",
"Chi Li",
"Daniel P. Robinson",
"Rene Vidal"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chong_You_A_Scalable_Exemplar-based_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chong_You_A_Scalable_Exemplar-based_ECCV_2018_paper.pdf | null | null | null | null | Subspace clustering methods based on expressing each data point as a linear combination of a few other data points (e.g., sparse subspace clustering) have become a popular tool for unsupervised learning due to their empirical success and theoretical guarantees. However, their performance can be affected by imbalanced d... |
Ye_Yuan_3D_Ego-Pose_Estimation_ECCV_2018_paper | 3D Ego-Pose Estimation via Imitation Learning | [
"Ye Yuan",
"Kris Kitani"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ye_Yuan_3D_Ego-Pose_Estimation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ye_Yuan_3D_Ego-Pose_Estimation_ECCV_2018_paper.pdf | null | null | null | null | Ego-pose estimation, i.e., estimating a person's 3D pose with a single wearable camera, has many potential applications in activity monitoring. For these applications, both accurate and physically plausible estimates are desired, with the latter often overlooked by existing work. Traditional computer vision-based appro... |
Satwik_Kottur_Visual_Coreference_Resolution_ECCV_2018_paper | Visual Coreference Resolution in Visual Dialog using Neural Module Networks | [
"Satwik Kottur",
"Jose M. F. Moura",
"Devi Parikh",
"Dhruv Batra",
"Marcus Rohrbach"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Satwik_Kottur_Visual_Coreference_Resolution_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Satwik_Kottur_Visual_Coreference_Resolution_ECCV_2018_paper.pdf | null | null | 1809.01816 | title_snapshot | Visual dialog entails answering a series of questions grounded in an image, using dialog history as context. In addition to the challenges found in visual question answering (VQA), which can be seen as one-round dialog, visual dialog encompasses several more. We focus on one such problem called ‘visual coreference reso... |
Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper | LSQ++: Lower running time and higher recall in multi-codebook quantization | [
"Julieta Martinez",
"Shobhit Zakhmi",
"Holger H. Hoos",
"James J. Little"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.pdf | null | null | null | null | Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases. Work in MCQ is heavily focused on lowering quantization error, thereby improving distance estimation and recall on benchmarks of visual descriptors at a fixed memory bud... |
Qianru_Sun_A_Hybrid_Model_ECCV_2018_paper | A Hybrid Model for Identity Obfuscation by Face Replacement | [
"Qianru Sun",
"Ayush Tewari",
"Weipeng Xu",
"Mario Fritz",
"Christian Theobalt",
"Bernt Schiele"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qianru_Sun_A_Hybrid_Model_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qianru_Sun_A_Hybrid_Model_ECCV_2018_paper.pdf | null | null | 1804.04779 | title_snapshot | As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition, becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with l... |
Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper | Depth-aware CNN for RGB-D Segmentation | [
"Weiyue Wang",
"Ulrich Neumann"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper.pdf | null | null | 1803.06791 | title_snapshot | Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial in... |
Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper | BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation | [
"Changqian Yu",
"Jingbo Wang",
"Chao Peng",
"Changxin Gao",
"Gang Yu",
"Nong Sang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.pdf | null | null | 1808.00897 | title_snapshot | Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet).... |
Yinda_Zhang_Active_Stereo_Net_ECCV_2018_paper | ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems | [
"Yinda Zhang",
"Sameh Khamis",
"Christoph Rhemann",
"Julien Valentin",
"Adarsh Kowdle",
"Vladimir Tankovich",
"Michael Schoenberg",
"Shahram Izadi",
"Thomas Funkhouser",
"Sean Fanello"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yinda_Zhang_Active_Stereo_Net_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yinda_Zhang_Active_Stereo_Net_ECCV_2018_paper.pdf | null | null | 1807.06009 | title_snapshot | In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of 1/30th of a pixel; it does not suffer from the common over-smoothing issues of previous a... |
Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper | Weakly- and Semi-Supervised Panoptic Segmentation | [
"Qizhu Li",
"Anurag Arnab",
"Philip H.S. Torr"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper.pdf | null | null | 1808.03575 | title_snapshot | We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does... |
Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper | Selfie Video Stabilization | [
"Jiyang Yu",
"Ravi Ramamoorthi"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.pdf | null | null | null | null | We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background mot... |
Jin-Seok_Park_Double_JPEG_Detection_ECCV_2018_paper | Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network | [
"Jinseok Park",
"Donghyeon Cho",
"Wonhyuk Ahn",
"Heung-Kyu Lee"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jin-Seok_Park_Double_JPEG_Detection_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jin-Seok_Park_Double_JPEG_Detection_ECCV_2018_paper.pdf | null | null | null | null | Double JPEG detection is essential for detecting various image manipulations. This paper proposes a novel deep convolutional neural network for double JPEG detection using statistical histogram features from each block with a vectorized quantization table. In contrast to previous methods, the proposed approach handles ... |
Tianshu_Yu_Incremental_Multi-graph_Matching_ECCV_2018_paper | Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering | [
"Tianshu Yu",
"Junchi Yan",
"Wei Liu",
"Baoxin Li"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Tianshu_Yu_Incremental_Multi-graph_Matching_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Tianshu_Yu_Incremental_Multi-graph_Matching_ECCV_2018_paper.pdf | null | null | null | null | Multi-graph matching refers to finding correspondences across graphs, which are traditionally solved by matching all the graphs in a single batch. However in real-world applications, graphs are often collected incrementally, rather than once for all. In this paper, we present an incremental multi-graph matching approac... |
Huizhong_Zhou_DeepTAM_Deep_Tracking_ECCV_2018_paper | DeepTAM: Deep Tracking and Mapping | [
"Huizhong Zhou",
"Benjamin Ummenhofer",
"Thomas Brox"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Huizhong_Zhou_DeepTAM_Deep_Tracking_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Huizhong_Zhou_DeepTAM_Deep_Tracking_ECCV_2018_paper.pdf | null | null | 1808.01900 | title_snapshot | We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motion... |
Nicholas_Rhinehart_R2P2_A_ReparameteRized_ECCV_2018_paper | R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting | [
"Nicholas Rhinehart",
"Kris M. Kitani",
"Paul Vernaza"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Nicholas_Rhinehart_R2P2_A_ReparameteRized_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Nicholas_Rhinehart_R2P2_A_ReparameteRized_ECCV_2018_paper.pdf | null | null | null | null | We propose a method to forecast a vehicle's ego-motion as a distribution over spatiotemporal paths, conditioned on features (e.g., from LIDAR and images) embedded in an overhead map. The method learns a policy inducing a distribution over simulated trajectories that is both diverse (produces most paths likely under the... |
Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper | SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters | [
"Yifan Xu",
"Tianqi Fan",
"Mingye Xu",
"Long Zeng",
"Yu Qiao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.pdf | null | null | 1803.11527 | title_snapshot | Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features ... |
Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper | CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images | [
"Sheng Guo",
"Weilin Huang",
"Haozhi Zhang",
"Chenfan Zhuang",
"Dengke Dong",
"Matthew R. Scott",
"Dinglong Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.pdf | null | null | 1808.01097 | title_snapshot | We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled rawly from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of ha... |
Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper | Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation | [
"Eddy Ilg",
"Tonmoy Saikia",
"Margret Keuper",
"Thomas Brox"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper.pdf | null | null | 1808.01838 | title_snapshot | Occlusions play an important role in optical flow and disparity estimation, since matching costs are not available in occluded areas and occlusions indicate motion boundaries. Moreover, occlusions are relevant for motion segmentation and scene flow estimation. In this paper, we present an efficient learning-based appro... |
Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper | Quantization Mimic: Towards Very Tiny CNN for Object Detection | [
"Yi Wei",
"Xinyu Pan",
"Hongwei Qin",
"Wanli Ouyang",
"Junjie Yan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper.pdf | null | null | 1805.02152 | title_snapshot | In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focu... |
Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper | Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation | [
"Zhaoyang Lv",
"Kihwan Kim",
"Alejandro Troccoli",
"Deqing Sun",
"James M. Rehg",
"Jan Kautz"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper.pdf | null | null | 1804.04259 | title_snapshot | Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from diff... |
Dong_Yang_Proximal_Dehaze-Net_A_ECCV_2018_paper | Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing | [
"Dong Yang",
"Jian Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dong_Yang_Proximal_Dehaze-Net_A_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dong_Yang_Proximal_Dehaze-Net_A_ECCV_2018_paper.pdf | null | null | null | null | Photos taken in hazy weather are usually covered with white masks and often lose important details. In this paper, we propose a novel deep learning approach for single image dehazing by learning dark channel and transmission priors. First, we build an energy model for dehazing using dark channel and transmission priors... |
Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper | Textual Explanations for Self-Driving Vehicles | [
"Jinkyu Kim",
"Anna Rohrbach",
"Trevor Darrell",
"John Canny",
"Zeynep Akata"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper.pdf | null | null | 1807.11546 | title_snapshot | Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations which allow end-users to understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely intro... |
Xuan_Chen_Focus_Segment_and_ECCV_2018_paper | Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation | [
"Xuan Chen",
"Jun Hao Liew",
"Wei Xiong",
"Chee-Kong Chui",
"Sim-Heng Ong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xuan_Chen_Focus_Segment_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xuan_Chen_Focus_Segment_and_ECCV_2018_paper.pdf | null | null | null | null | In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems. In this paper, we propose a novel end-to-end trainable network named FSENet to address the aforementioned issues. The proposed FSENet has a tumor region pooling component to restrict the prediction... |
Ahmet_Iscen_Local_Orthogonal-Group_Testing_ECCV_2018_paper | Local Orthogonal-Group Testing | [
"Ahmet Iscen",
"Ondrej Chum"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ahmet_Iscen_Local_Orthogonal-Group_Testing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ahmet_Iscen_Local_Orthogonal-Group_Testing_ECCV_2018_paper.pdf | null | null | 1807.09848 | title_snapshot | This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval. Within the group testing framework we propose an efficient off-line construction of the search structures. The linear-time complexity orthogonal grouping increases the probability that at most one element from ... |
Eunji_Chong_Connecting_Gaze_Scene_ECCV_2018_paper | Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency | [
"Eunji Chong",
"Nataniel Ruiz",
"Yongxin Wang",
"Yun Zhang",
"Agata Rozga",
"James M. Rehg"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Eunji_Chong_Connecting_Gaze_Scene_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Eunji_Chong_Connecting_Gaze_Scene_ECCV_2018_paper.pdf | null | null | 1807.10437 | title_snapshot | This paper addresses the challenging problem of estimating the general visual attention of people in images. Our proposed method is designed to work across multiple naturalistic social scenarios and provides a full picture of the subject’s attention and gaze. In contrast, earlier works on gaze and attention estimation ... |
Xihui_Liu_Show_Tell_and_ECCV_2018_paper | Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data | [
"Xihui Liu",
"Hongsheng Li",
"Jing Shao",
"Dapeng Chen",
"Xiaogang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xihui_Liu_Show_Tell_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xihui_Liu_Show_Tell_and_ECCV_2018_paper.pdf | null | null | 1803.08314 | title_snapshot | The aim of image captioning is to generate captions by machine to describe image contents. Despite many efforts, generating discriminative captions for images remains non-trivial. Most traditional approaches imitate the language structure patterns, thus tend to fall into a stereotype of replicating frequent phrases or ... |
Yuan-Ting_Hu_VideoMatch_Matching_based_ECCV_2018_paper | VideoMatch: Matching based Video Object Segmentation | [
"Yuan-Ting Hu",
"Jia-Bin Huang",
"Alexander G. Schwing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yuan-Ting_Hu_VideoMatch_Matching_based_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yuan-Ting_Hu_VideoMatch_Matching_based_ECCV_2018_paper.pdf | null | null | 1809.01123 | title_snapshot | Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require... |
Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper | Unsupervised Video Object Segmentation with Motion-based Bilateral Networks | [
"Siyang Li",
"Bryan Seybold",
"Alexey Vorobyov",
"Xuejing Lei",
"C.-C. Jay Kuo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.pdf | null | null | null | null | In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false posi... |
Sebastian_Bullinger_3D_Vehicle_Trajectory_ECCV_2018_paper | 3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints | [
"Sebastian Bullinger",
"Christoph Bodensteiner",
"Michael Arens",
"Rainer Stiefelhagen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sebastian_Bullinger_3D_Vehicle_Trajectory_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sebastian_Bullinger_3D_Vehicle_Trajectory_ECCV_2018_paper.pdf | null | null | null | null | We present a framework to reconstruct three-dimensional vehicle trajectories using monocular video data. We track two-dimensional vehicle shapes on pixel level exploiting instance-aware semantic segmentation techniques and optical flow cues. We apply Structure from Motion techniques to vehicle and background images to ... |
XU_YANG_Shuffle-Then-Assemble_Learning_Object-Agnostic_ECCV_2018_paper | Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features | [
"Xu Yang",
"Hanwang Zhang",
"Jianfei Cai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/XU_YANG_Shuffle-Then-Assemble_Learning_Object-Agnostic_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/XU_YANG_Shuffle-Then-Assemble_Learning_Object-Agnostic_ECCV_2018_paper.pdf | null | null | 1808.00171 | title_snapshot | Due to fact that it is prohibitively expensive to completely annotate visual relationships, ie, the (obj1, rel, obj2) triplets, relationship models are inevitably biased to object classes of limited pairwise patterns, leading to poor generalization to rare or unseen object combinations. Therefore, we are interested in ... |
Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper | Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors | [
"Dmitry Baranchuk",
"Artem Babenko",
"Yury Malkov"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper.pdf | null | null | 1802.02422 | title_snapshot | This work addresses the problem of billion-scale nearest neighbor search. The state-of-the-art retrieval systems for billion-scale databases are currently based on the inverted multi-index, the recently proposed generalization of the inverted index structure. The multi-index provides a very fine-grained partition of th... |
Gregoire_Payen_de_La_Garanderie_Eliminating_the_Dreaded_ECCV_2018_paper | Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery | [
"Greire Payen de La Garanderie",
"Amir Atapour Abarghouei",
"Toby P. Breckon"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Gregoire_Payen_de_La_Garanderie_Eliminating_the_Dreaded_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Gregoire_Payen_de_La_Garanderie_Eliminating_the_Dreaded_ECCV_2018_paper.pdf | null | null | 1808.06253 | title_snapshot | Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360° panoramic cameras. We present an approach to adapt contemporary deep... |
Pei_Wang_Towards_Realistic_Predictors_ECCV_2018_paper | Towards Realistic Predictors | [
"Pei Wang",
"Nuno Vasconcelos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Pei_Wang_Towards_Realistic_Predictors_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Pei_Wang_Towards_Realistic_Predictors_ECCV_2018_paper.pdf | null | null | null | null | A new class of predictors, denoted realistic predictors, is defined. These are predictors that, like humans, assess the difficulty of examples, reject to work on those that are deemed too hard, but guarantee good performance on the ones they operate on. In this paper, we talk about a particular case of it, realistic cl... |
Nikolaos_Passalis_Learning_Deep_Representations_ECCV_2018_paper | Learning Deep Representations with Probabilistic Knowledge Transfer | [
"Nikolaos Passalis",
"Anastasios Tefas"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Nikolaos_Passalis_Learning_Deep_Representations_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Nikolaos_Passalis_Learning_Deep_Representations_ECCV_2018_paper.pdf | null | null | 1803.10837 | title_snapshot | Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT methods are tailored towards classification tasks and they cannot be used efficiently for other representation learning tasks. In this paper we ... |
Jongbin_Ryu_DFT-based_Transformation_Invariant_ECCV_2018_paper | DFT-based Transformation Invariant Pooling Layer for Visual Classification | [
"Jongbin Ryu",
"Ming-Hsuan Yang",
"Jongwoo Lim"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jongbin_Ryu_DFT-based_Transformation_Invariant_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jongbin_Ryu_DFT-based_Transformation_Invariant_ECCV_2018_paper.pdf | null | null | null | null | We propose a novel discrete Fourier transform-based pooling layer for convolutional neural networks. The DFT magnitude pooling replaces the traditional max/average pooling layer between the convolution and fully-connected layers to retain translation invariance and shape preserving (aware of shape difference) propertie... |
Relja_Arandjelovic_Objects_that_Sound_ECCV_2018_paper | Objects that Sound | [
"Relja Arandjelovic",
"Andrew Zisserman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Relja_Arandjelovic_Objects_that_Sound_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Relja_Arandjelovic_Objects_that_Sound_ECCV_2018_paper.pdf | null | null | 1712.06651 | title_snapshot | In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that can localize the object that sounds in an image, given the audio signal. We achieve both these objectives by training from unlabelled video ... |
Francisco_M._Castro_End-to-End_Incremental_Learning_ECCV_2018_paper | End-to-End Incremental Learning | [
"Francisco M. Castro",
"Manuel J. Marin-Jimenez",
"Nicolas Guil",
"Cordelia Schmid",
"Karteek Alahari"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Francisco_M._Castro_End-to-End_Incremental_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Francisco_M._Castro_End-to-End_Incremental_Learning_ECCV_2018_paper.pdf | null | null | 1807.09536 | title_snapshot | Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the e... |
Safa_Cicek_SaaS_Speed_as_ECCV_2018_paper | SaaS: Speed as a Supervisor for Semi-supervised Learning | [
"Safa Cicek",
"Alhussein Fawzi",
"Stefano Soatto"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Safa_Cicek_SaaS_Speed_as_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Safa_Cicek_SaaS_Speed_as_ECCV_2018_paper.pdf | null | null | 1805.00980 | title_snapshot | We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentag... |
Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper | Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network | [
"Woojae Kim",
"Jongyoo Kim",
"Sewoong Ahn",
"Jinwoo Kim",
"Sanghoon Lee"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper.pdf | null | null | null | null | Incorporating spatio-temporal human visual perception into video quality assessment (VQA) remains a formidable issue. Previous statistical or computational models of spatio-temporal perception have limitations to be applied to the general VQA algorithms. In this paper, we propose a novel full-reference (FR) VQA framewo... |
Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper | Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering | [
"Medhini Narasimhan",
"Alexander G. Schwing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.pdf | null | null | 1809.01124 | title_snapshot | Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general kno... |
Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper | Deep Volumetric Video From Very Sparse Multi-View Performance Capture | [
"Zeng Huang",
"Tianye Li",
"Weikai Chen",
"Yajie Zhao",
"Jun Xing",
"Chloe LeGendre",
"Linjie Luo",
"Chongyang Ma",
"Hao Li"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper.pdf | null | null | null | null | We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system. We focus on a template-free, per-frame 3D surface reconstruction from as few as three RGB sensors, where conventional visual hull or multi-view stereo methods would fail. Sta... |
Huayi_Zeng_Neural_Procedural_Reconstruction_ECCV_2018_paper | Neural Procedural Reconstruction for Residential Buildings | [
"Huayi Zeng",
"Jiaye Wu",
"Yasutaka Furukawa"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Huayi_Zeng_Neural_Procedural_Reconstruction_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Huayi_Zeng_Neural_Procedural_Reconstruction_ECCV_2018_paper.pdf | null | null | null | null | This paper proposes a novel 3D reconstruction approach, dubbed Neural Procedural Reconstruction (NPR), which trains deep neural networks to procedurally apply shape grammar rules and reconstruct CAD-quality models from 3D points. In contrast to Procedural Modeling (PM), which randomly applies shape grammar rules to syn... |
Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper | Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition | [
"Junwu Weng",
"Mengyuan Liu",
"Xudong Jiang",
"Junsong Yuan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper.pdf | null | null | null | null | The representation of 3D pose plays a critical role for 3D body action and hand gesture recognition. Rather than directly representing the 3D pose using its joint locations, in this paper, we propose Deformable Pose Traversal Convolution which applies one-dimensional convolution to traverse the 3D pose to represent it.... |
XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper | A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising | [
"Jun Xu",
"Lei Zhang",
"David Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper.pdf | null | null | 1807.04364 | title_snapshot | Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modeled by simple analytical distributions. As a result, many state-of-the-art denoising methods in li... |
yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper | Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition | [
"Yitong Wang",
"Dihong Gong",
"Zheng Zhou",
"Xing Ji",
"Hao Wang",
"Zhifeng Li",
"Wei Liu",
"Tong Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper.pdf | null | null | 1810.07599 | title_snapshot | As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by aging, in this paper we propose a novel approach (namely, Orthogonal... |
Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper | Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection | [
"Hongmei Song",
"Wenguan Wang",
"Sanyuan Zhao",
"Jianbing Shen",
"Kin-Man Lam"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.pdf | null | null | null | null | This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is first designed for simultaneously extracting spatial features at multiple scales. These spatial fea... |
Clement_Godard_Deep_Burst_Denoising_ECCV_2018_paper | Deep Burst Denoising | [
"Clement Godard",
"Kevin Matzen",
"Matt Uyttendaele"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Clement_Godard_Deep_Burst_Denoising_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Clement_Godard_Deep_Burst_Denoising_ECCV_2018_paper.pdf | null | null | 1712.05790 | title_snapshot | Noise is an inherent issue of low-light image capture, which is worsened on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in low-light situations is to increase the shutter time, allowing each photosite to integrate more light and decrease noise variance. However, the... |
Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper | Learning to Separate Object Sounds by Watching Unlabeled Video | [
"Ruohan Gao",
"Rogerio Feris",
"Kristen Grauman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.pdf | null | null | 1804.01665 | title_snapshot | Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources together. We propose to learn audio-visual object models from unlabeled video, then exploit the visual c... |
Samuel_Albanie_Learnable_PINs_Cross-Modal_ECCV_2018_paper | Learnable PINs: Cross-Modal Embeddings for Person Identity | [
"Arsha Nagrani",
"Samuel Albanie",
"Andrew Zisserman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Samuel_Albanie_Learnable_PINs_Cross-Modal_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Samuel_Albanie_Learnable_PINs_Cross-Modal_ECCV_2018_paper.pdf | null | null | 1805.00833 | title_snapshot | We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any ide... |
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