paper_id stringlengths 33 78 | title stringlengths 15 146 | authors listlengths 1 16 | ecva_url stringlengths 92 137 | pdf_url stringlengths 94 139 | supp_url stringclasses 0
values | doi stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | abstract large_stringlengths 474 1.99k |
|---|---|---|---|---|---|---|---|---|---|
Shaofei_Wang_Accelerating_Dynamic_Programs_ECCV_2018_paper | Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation | [
"Shaofei Wang",
"Alexander Ihler",
"Konrad Kording",
"Julian Yarkony"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shaofei_Wang_Accelerating_Dynamic_Programs_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shaofei_Wang_Accelerating_Dynamic_Programs_ECCV_2018_paper.pdf | null | null | null | null | We present a novel approach to solve dynamic programs (DP), which are frequent in computer vision, on tree-structured graphs with exponential node state space. Typical DP approaches have to enumerate the joint state space of two adjacent nodes on every edge of the tree to compute the optimal messages. Here we propose a... |
Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper | Multi-scale Residual Network for Image Super-Resolution | [
"Juncheng Li",
"Faming Fang",
"Kangfu Mei",
"Guixu Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.pdf | null | null | null | null | Recent studies have shown that deep neural networks can significantly improve the quality of single-image super-resolution. Current researches tend to use deeper convolutional neural networks to enhance performance. However, blindly increasing the depth of the network cannot ameliorate the network effectively. Worse st... |
Yinlong_Liu_Efficient_Global_Point_ECCV_2018_paper | Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search | [
"Yinlong Liu",
"Chen Wang",
"Zhijian Song",
"Manning Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yinlong_Liu_Efficient_Global_Point_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yinlong_Liu_Efficient_Global_Point_ECCV_2018_paper.pdf | null | null | null | null | Three-dimensional rigid point cloud registration has many applications in computer vision and robotics. Local methods tend to fail, causing global methods to be needed, when the relative transformation is large or the overlap ratio is small. Most existing global methods utilize BnB optimization over the 6D parameter sp... |
Yongcheng_Jing_Stroke_Controllable_Fast_ECCV_2018_paper | Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields | [
"Yongcheng Jing",
"Yang Liu",
"Yezhou Yang",
"Zunlei Feng",
"Yizhou Yu",
"Dacheng Tao",
"Mingli Song"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yongcheng_Jing_Stroke_Controllable_Fast_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yongcheng_Jing_Stroke_Controllable_Fast_ECCV_2018_paper.pdf | null | null | 1802.07101 | title_snapshot | The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continu... |
Xiangyun_Zhao_A_Modulation_Module_ECCV_2018_paper | A Modulation Module for Multi-task Learning with Applications in Image Retrieval | [
"Xiangyun Zhao",
"Haoxiang Li",
"Xiaohui Shen",
"Xiaodan Liang",
"Ying Wu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiangyun_Zhao_A_Modulation_Module_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiangyun_Zhao_A_Modulation_Module_ECCV_2018_paper.pdf | null | null | 1807.06708 | title_snapshot | Multi-task learning has been widely adopted in many computer vision tasks to improve overall computation efficiency or boost the performance of individual tasks, under the assumption that those tasks are correlated and complementary to each other. However, the relationships between the tasks are complicated in practice... |
Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper | Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance | [
"Zhixin Shu",
"Mihir Sahasrabudhe",
"Riza Alp Guler",
"Dimitris Samaras",
"Nikos Paragios",
"Iasonas Kokkinos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.pdf | null | null | 1806.06503 | title_snapshot | In this work we introduce the Deforming Autoencoder, a generative model for images that disentangles shape from appearance in a latent representation space that is learned in a fully unsupervised manner. As in the deformable template paradigm, shape is represented as a diffeomorphism between a canonical coordinate syst... |
Dinesh_Jayaraman_ShapeCodes_Self-Supervised_Feature_ECCV_2018_paper | ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids | [
"Dinesh Jayaraman",
"Ruohan Gao",
"Kristen Grauman"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Dinesh_Jayaraman_ShapeCodes_Self-Supervised_Feature_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Dinesh_Jayaraman_ShapeCodes_Self-Supervised_Feature_ECCV_2018_paper.pdf | null | null | 1709.00505 | title_snapshot | We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this id... |
Xingping_Dong_Triplet_Loss_with_ECCV_2018_paper | Triplet Loss in Siamese Network for Object Tracking | [
"Xingping Dong",
"Jianbing Shen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xingping_Dong_Triplet_Loss_with_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xingping_Dong_Triplet_Loss_with_ECCV_2018_paper.pdf | null | null | null | null | Object tracking is still a critical and challenging problem with many applications in computer vision. For this challenge, more and more researchers pay attention to applying deep learning to get powerful feature for better tracking accuracy. In this paper, a novel triplet loss is proposed to extract expressive deep fe... |
Yantao_Shen_Person_Re-identification_with_ECCV_2018_paper | Person Re-identification with Deep Similarity-Guided Graph Neural Network | [
"Yantao Shen",
"Hongsheng Li",
"Shuai Yi",
"Dapeng Chen",
"Xiaogang Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yantao_Shen_Person_Re-identification_with_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yantao_Shen_Person_Re-identification_with_ECCV_2018_paper.pdf | null | null | 1807.09975 | title_snapshot | The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different pr... |
Konstantinos-Nektarios_Lianos_VSO_Visual_Semantic_ECCV_2018_paper | VSO: Visual Semantic Odometry | [
"Konstantinos-Nektarios Lianos",
"Johannes L. Schonberger",
"Marc Pollefeys",
"Torsten Sattler"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Konstantinos-Nektarios_Lianos_VSO_Visual_Semantic_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Konstantinos-Nektarios_Lianos_VSO_Visual_Semantic_ECCV_2018_paper.pdf | null | null | null | null | Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation. Current state-of-the-art direct and indirect methods use short-term tracking to obtain continuous frame-to-frame constraints, while long-term constraints are establi... |
Andrew_Gilbert_Volumetric_performance_capture_ECCV_2018_paper | Volumetric performance capture from minimal camera viewpoints | [
"Andrew Gilbert",
"Marco Volino",
"John Collomosse",
"Adrian Hilton"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Andrew_Gilbert_Volumetric_performance_capture_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Andrew_Gilbert_Volumetric_performance_capture_ECCV_2018_paper.pdf | null | null | 1807.01950 | title_snapshot | We present a convolutional autoencoder that enables high fidelity volumetric reconstructions of human performance to be captured from multi-view video comprising only a small set of camera views. Our method yields similar end-to-end reconstruction error to that of a probabilistic visual hull computed using significantl... |
Xiaolong_Wang_Videos_as_Space-Time_ECCV_2018_paper | Videos as Space-Time Region Graphs | [
"Xiaolong Wang",
"Abhinav Gupta"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xiaolong_Wang_Videos_as_Space-Time_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xiaolong_Wang_Videos_as_Space-Time_ECCV_2018_paper.pdf | null | null | 1806.01810 | title_snapshot | How do humans recognize the action "opening a book"? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph ... |
Renjiao_Yi_Faces_as_Lighting_ECCV_2018_paper | Faces as Lighting Probes via Unsupervised Deep Highlight Extraction | [
"Renjiao Yi",
"Chenyang Zhu",
"Ping Tan",
"Stephen Lin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Renjiao_Yi_Faces_as_Lighting_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Renjiao_Yi_Faces_as_Lighting_ECCV_2018_paper.pdf | null | null | 1803.06340 | title_snapshot | We present a method for estimating detailed scene illumination using human faces in a single image. In contrast to previous works that estimate lighting in terms of low-order basis functions or distant point lights, our technique estimates illumination at a higher precision in the form of a non-parametric environment m... |
Donghoon_Lee_Unsupervised_holistic_image_ECCV_2018_paper | Unsupervised holistic image generation from key local patches | [
"Donghoon Lee",
"Sangdoo Yun",
"Sungjoon Choi",
"Hwiyeon Yoo",
"Ming-Hsuan Yang",
"Songhwai Oh"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Donghoon_Lee_Unsupervised_holistic_image_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Donghoon_Lee_Unsupervised_holistic_image_ECCV_2018_paper.pdf | null | null | 1703.10730 | title_snapshot | We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locatio... |
Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper | Visual Text Correction | [
"Amir Mazaheri",
"Mubarak Shah"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper.pdf | null | null | 1801.01967 | title_snapshot | Videos, images, and sentences are mediums that can express the same semantics. One can imagine a picture by reading a sentence or can describe a scene with some words. However, even small changes in a sentence can cause a significant semantic inconsistency with the corresponding video/image. For example, by changing th... |
Taihong_Xiao_ELEGANT_Exchanging_Latent_ECCV_2018_paper | ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes | [
"Taihong Xiao",
"Jiapeng Hong",
"Jinwen Ma"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Taihong_Xiao_ELEGANT_Exchanging_Latent_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Taihong_Xiao_ELEGANT_Exchanging_Latent_ECCV_2018_paper.pdf | null | null | 1803.10562 | title_snapshot | Recent studies on face attribute transfer have achieved great success. A lot of models are able to transfer face attributes with an input image. However, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low q... |
Hyojin_Bahng_Coloring_with_Words_ECCV_2018_paper | Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation | [
"Hyojin Bahng",
"Seungjoo Yoo",
"Wonwoong Cho",
"David Keetae Park",
"Ziming Wu",
"Xiaojuan Ma",
"Jaegul Choo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hyojin_Bahng_Coloring_with_Words_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hyojin_Bahng_Coloring_with_Words_ECCV_2018_paper.pdf | null | null | 1804.04128 | title_snapshot | This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a senten... |
Minho_Shim_Teaching_Machines_to_ECCV_2018_paper | Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks | [
"Minho Shim",
"Young Hwi Kim",
"Kyungmin Kim",
"Seon Joo Kim"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.pdf | null | null | null | null | A major obstacle in teaching machines to understand videos is the lack of training data, as creating temporal annotations for long videos requires a huge amount of human effort. To this end, we introduce a new large-scale baseball video dataset called the BBDB, which is produced semi-automatically by using play-by-play... |
Aashish_Sharma_Into_the_Twilight_ECCV_2018_paper | Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization | [
"Aashish Sharma",
"Loong-Fah Cheong"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Aashish_Sharma_Into_the_Twilight_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Aashish_Sharma_Into_the_Twilight_ECCV_2018_paper.pdf | null | null | null | null | We present a joint Structure-Stereo optimization model that is robust for disparity estimation under low-light conditions. Eschewing the traditional denoising approach - which we show to be ineffective for stereo due to its artefacts and the questionable use of the PSNR metric, we propose to instead rely on structures ... |
Kripasindhu_Sarkar_Learning_3D_shapes_ECCV_2018_paper | Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks | [
"Kripasindhu Sarkar",
"Basavaraj Hampiholi",
"Kiran Varanasi",
"Didier Stricker"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kripasindhu_Sarkar_Learning_3D_shapes_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kripasindhu_Sarkar_Learning_3D_shapes_ECCV_2018_paper.pdf | null | null | 1807.08485 | title_snapshot | We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height maps (MLH) where at each grid location, we store multiple instances of height maps, thereby representing 3D shape detail that is hidden behind several layers of occlusion. We pr... |
Abhimanyu_Dubey_Coreset-Based_Convolutional_Neural_ECCV_2018_paper | Coreset-Based Neural Network Compression | [
"Abhimanyu Dubey",
"Moitreya Chatterjee",
"Narendra Ahuja"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Abhimanyu_Dubey_Coreset-Based_Convolutional_Neural_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Abhimanyu_Dubey_Coreset-Based_Convolutional_Neural_ECCV_2018_paper.pdf | null | null | 1807.09810 | title_snapshot | We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression. Our method requires no retraining, is easy to implement, and o... |
Liang_Mi_Variational_Wasserstein_Clustering_ECCV_2018_paper | Variational Wasserstein Clustering | [
"Liang Mi",
"Wen Zhang",
"Xianfeng Gu",
"Yalin Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Liang_Mi_Variational_Wasserstein_Clustering_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Liang_Mi_Variational_Wasserstein_Clustering_ECCV_2018_paper.pdf | null | null | 1806.09045 | title_snapshot | We propose a new clustering method based on optimal transportation. We discuss the connection between optimal transportation and k-means clustering, solve optimal transportation with the variational principle, and investigate the use of power diagrams as transportation plans for aggregating arbitrary domains into a fix... |
Mingze_Xu_Joint_Person_Segmentation_ECCV_2018_paper | Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos | [
"Mingze Xu",
"Chenyou Fan",
"Yuchen Wang",
"Michael S. Ryoo",
"David J. Crandall"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Mingze_Xu_Joint_Person_Segmentation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Mingze_Xu_Joint_Person_Segmentation_ECCV_2018_paper.pdf | null | null | 1803.11217 | title_snapshot | In a world of pervasive cameras, public spaces are often captured from multiple perspectives by cameras of different types, both fixed and mobile. An important problem is to organize these heterogeneous collections of videos by finding connections between them, such as identifying correspondences between the people app... |
Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper | Zero-shot keyword spotting for visual speech recognition in-the-wild | [
"Themos Stafylakis",
"Georgios Tzimiropoulos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper.pdf | null | null | 1807.08469 | title_snapshot | Visual keyword spotting (KWS) is the problem of estimating whether a text query occurs in a given recording using only video information. This paper focuses on visual KWS for words unseen during training, a real-world, practical setting which so far has received no attention by the community. To this end, we devise an ... |
Wonmin_Byeon_ContextVP_Fully_Context-Aware_ECCV_2018_paper | ContextVP: Fully Context-Aware Video Prediction | [
"Wonmin Byeon",
"Qin Wang",
"Rupesh Kumar Srivastava",
"Petros Koumoutsakos"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wonmin_Byeon_ContextVP_Fully_Context-Aware_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wonmin_Byeon_ContextVP_Fully_Context-Aware_ECCV_2018_paper.pdf | null | null | 1710.08518 | title_snapshot | Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past in... |
Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper | Open Set Domain Adaptation by Backpropagation | [
"Kuniaki Saito",
"Shohei Yamamoto",
"Yoshitaka Ushiku",
"Tatsuya Harada"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.pdf | null | null | 1804.10427 | title_snapshot | Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Most of them are proposed for closed-set scenario, where the source and the target domain completely share the class of their samples. However, in practice, a target domain can contain ... |
Benjamin_Hepp_Learn-to-Score_Efficient_3D_ECCV_2018_paper | Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility | [
"Benjamin Hepp",
"Debadeepta Dey",
"Sudipta N. Sinha",
"Ashish Kapoor",
"Neel Joshi",
"Otmar Hilliges"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Benjamin_Hepp_Learn-to-Score_Efficient_3D_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Benjamin_Hepp_Learn-to-Score_Efficient_3D_ECCV_2018_paper.pdf | null | null | 1806.10354 | title_snapshot | Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predict and maximize usefu... |
Chuhui_Xue_Accurate_Scene_Text_ECCV_2018_paper | Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping | [
"Chuhui Xue",
"Shijian Lu",
"Fangneng Zhan"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chuhui_Xue_Accurate_Scene_Text_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chuhui_Xue_Accurate_Scene_Text_ECCV_2018_paper.pdf | null | null | 1807.03547 | title_snapshot | This paper presents a scene text detection technique that exploits bootstrapping and text border semantics for accurate localization of texts in scenes. A novel bootstrapping technique is designed which samples multiple ‘subsections’ of a word or text line and accordingly relieves the constraint of limited training dat... |
Filippos_Kokkinos_Deep_Image_Demosaicking_ECCV_2018_paper | Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks | [
"Filippos Kokkinos",
"Stamatios Lefkimmiatis"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Filippos_Kokkinos_Deep_Image_Demosaicking_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Filippos_Kokkinos_Deep_Image_Demosaicking_ECCV_2018_paper.pdf | null | null | 1803.05215 | title_snapshot | Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstruction... |
Yipu_Zhao_Good_Line_Cutting_ECCV_2018_paper | Good Line Cutting: towards Accurate Pose Tracking of Line-assisted VO/VSLAM | [
"Yipu Zhao",
"Patricio A. Vela"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yipu_Zhao_Good_Line_Cutting_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yipu_Zhao_Good_Line_Cutting_ECCV_2018_paper.pdf | null | null | null | null | This paper tackles a problem in line-assisted VO/VSLAM: accurately solving the least squares pose optimization with unreliable 3D line input. The solution we present is good line cutting, which extracts the most-informative sub-segment from each 3D line for use within the pose optimization formulation. By studying the ... |
Changan_Chen_Constraints_Matter_in_ECCV_2018_paper | Constraint-Aware Deep Neural Network Compression | [
"Changan Chen",
"Frederick Tung",
"Naveen Vedula",
"Greg Mori"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Changan_Chen_Constraints_Matter_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Changan_Chen_Constraints_Matter_in_ECCV_2018_paper.pdf | null | null | null | null | Deep neural network compression has the potential to bring modern resource-hungry deep networks to resource-limited devices. However, in many of the most compelling deployment scenarios of compressed deep networks, the operational constraints matter: for example, a pedestrian detection network on a self-driving car may... |
Shi_Chen_Boosted_Attention_Leveraging_ECCV_2018_paper | Boosted Attention: Leveraging Human Attention for Image Captioning | [
"Shi Chen",
"Qi Zhao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Shi_Chen_Boosted_Attention_Leveraging_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Shi_Chen_Boosted_Attention_Leveraging_ECCV_2018_paper.pdf | null | null | 1904.00767 | title_snapshot | Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest. Existing models typically rely on top-down language information and learn attention implicitly by optimizing the captioning objectives. While somewhat effective, the learned ... |
Meredith_Hu_Understanding_Perceptual_and_ECCV_2018_paper | Understanding Perceptual and Conceptual Fluency at a Large Scale | [
"Shengli Hu",
"Ali Borji"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Meredith_Hu_Understanding_Perceptual_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Meredith_Hu_Understanding_Perceptual_and_ECCV_2018_paper.pdf | null | null | null | null | We create a dataset of 543,758 logo designs spanning 39 industrial categories and 216 countries. We experiment and compare how different deep convolutional neural network (hereafter, DCNN) architectures, pretraining protocols, and weight initializations perform in predicting design memorability and likability. We propo... |
Karim_Ahmed_MaskConnect_Connectivity_Learning_ECCV_2018_paper | MaskConnect: Connectivity Learning by Gradient Descent | [
"Karim Ahmed",
"Lorenzo Torresani"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Karim_Ahmed_MaskConnect_Connectivity_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Karim_Ahmed_MaskConnect_Connectivity_Learning_ECCV_2018_paper.pdf | null | null | 1807.11473 | title_snapshot | Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture search. To combat the complexity of design choices, prior work has adopted the principle of modularized design which consists in defining t... |
Ting_Yao_Exploring_Visual_Relationship_ECCV_2018_paper | Exploring Visual Relationship for Image Captioning | [
"Ting Yao",
"Yingwei Pan",
"Yehao Li",
"Tao Mei"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ting_Yao_Exploring_Visual_Relationship_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ting_Yao_Exploring_Visual_Relationship_ECCV_2018_paper.pdf | null | null | 1809.07041 | title_snapshot | It is always well believed that modeling relationships between objects would be helpful for representing and eventually describing an image. Nevertheless, there has not been evidence in support of the idea on image description generation. In this paper, we introduce a new design to explore the connections between objec... |
Humam_Alwassel_Diagnosing_Error_in_ECCV_2018_paper | Diagnosing Error in Temporal Action Detectors | [
"Humam Alwassel",
"Fabian Caba Heilbron",
"Victor Escorcia",
"Bernard Ghanem"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Humam_Alwassel_Diagnosing_Error_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Humam_Alwassel_Diagnosing_Error_in_ECCV_2018_paper.pdf | null | null | 1807.10706 | title_snapshot | Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors i... |
Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper | Efficient Semantic Scene Completion Network with Spatial Group Convolution | [
"Jiahui Zhang",
"Hao Zhao",
"Anbang Yao",
"Yurong Chen",
"Li Zhang",
"Hongen Liao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.pdf | null | null | 1907.05091 | title_snapshot | We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separat... |
Quanlong_Zheng_Task-driven_Webpage_Saliency_ECCV_2018_paper | Task-driven Webpage Saliency | [
"Quanlong Zheng",
"Jianbo Jiao",
"Ying Cao",
"Rynson W.H. Lau"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Quanlong_Zheng_Task-driven_Webpage_Saliency_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Quanlong_Zheng_Task-driven_Webpage_Saliency_ECCV_2018_paper.pdf | null | null | null | null | In this paper, we present an end-to-end learning framework for predicting task-driven visual saliency on webpages. Given a webpage, we propose a convolutional neural network to predict where people look at it under different task conditions. Inspired by the observation that given a specific task, human attention is str... |
Di_Lin_Multi-Scale_Context_Intertwining_ECCV_2018_paper | Multi-Scale Context Intertwining for Semantic Segmentation | [
"Di Lin",
"Yuanfeng Ji",
"Dani Lischinski",
"Daniel Cohen-Or",
"Hui Huang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Di_Lin_Multi-Scale_Context_Intertwining_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Di_Lin_Multi-Scale_Context_Intertwining_ECCV_2018_paper.pdf | null | null | null | null | Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context. In today’s age of pre-trained deep networks and their powerful convolutional features, state-of-the-art semantic segmentation approaches differ mostly in how they choose to combine ... |
Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper | Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video | [
"Ernesto Brau",
"Jinyan Guan",
"Tanya Jeffries",
"Kobus Barnard"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper.pdf | null | null | null | null | We develop using person gaze direction for scene understanding. In particular, we use intersecting gazes to learn 3D locations that people tend to look at, which is analogous to having multiple camera views. The 3D locations that we discover need not be visible to the camera. Conversely, knowing 3D locations of scene e... |
Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper | HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs | [
"Zerong Zheng",
"Tao Yu",
"Hao Li",
"Kaiwen Guo",
"Qionghai Dai",
"Lu Fang",
"Yebin Liu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.pdf | null | null | null | null | We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs). The proposed method combines non-rigid surface tracking and volumetric surface fusion to simultaneously reconstruct challenging motions, detailed geometries... |
Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper | Macro-Micro Adversarial Network for Human Parsing | [
"Yawei Luo",
"Zhedong Zheng",
"Liang Zheng",
"Tao Guan",
"Junqing Yu",
"Yi Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.pdf | null | null | 1807.08260 | title_snapshot | In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial network tackles the two problems using a single discriminator. However, the two types of parsing inconsistency are generated by distinct mech... |
Sunghun_Kang_Pivot_Correlational_Neural_ECCV_2018_paper | Pivot Correlational Neural Network for Multimodal Video Categorization | [
"Sunghun Kang",
"Junyeong Kim",
"Hyunsoo Choi",
"Sungjin Kim",
"Chang D. Yoo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sunghun_Kang_Pivot_Correlational_Neural_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sunghun_Kang_Pivot_Correlational_Neural_ECCV_2018_paper.pdf | null | null | null | null | This paper considers an architecture for multimodal video categorization referred to as Pivot Correlational Neural Network (Pivot CorrNN). The architecture is trained to maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific stream in the net... |
Thomas_Holzmann_Semantically_Aware_Urban_ECCV_2018_paper | Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization | [
"Thomas Holzmann",
"Michael Maurer",
"Friedrich Fraundorfer",
"Horst Bischof"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Thomas_Holzmann_Semantically_Aware_Urban_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Thomas_Holzmann_Semantically_Aware_Urban_ECCV_2018_paper.pdf | null | null | null | null | We propose a method for urban 3D reconstruction, which incorporates semantic information and plane priors within the reconstruction process in order to generate visually appealing 3D models. We introduce a plane detection algorithm using 3D lines, which detects a more complete and less spurious plane set compared to po... |
Sheng-Wei_Huang_AugGAN_Cross_Domain_ECCV_2018_paper | AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation | [
"Sheng-Wei Huang",
"Che-Tsung Lin",
"Shu-Ping Chen",
"Yen-Yi Wu",
"Po-Hao Hsu",
"Shang-Hong Lai"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sheng-Wei_Huang_AugGAN_Cross_Domain_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sheng-Wei_Huang_AugGAN_Cross_Domain_ECCV_2018_paper.pdf | null | null | null | null | Deep learning based image-to-image translation methods aim at learning the joint distribution of the two domains and finding transformations between them. Despite recent GAN (Generative Adversarial Network) based methods have shown compelling visual results, they are prone to fail at preserving image-objects and mainta... |
Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper | Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training | [
"Yang Zou",
"Zhiding Yu",
"B.V.K. Vijaya Kumar",
"Jinsong Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf | null | null | 1810.07911 | title_judge | Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks. Despite such progress, thesemodels often face challenges in real world “wild tasks” where large differ-ence between labeled training/source data and unseen test/target dataexists. In particular, such difference is oft... |
Yin_Xia_Fictitious_GAN_Training_ECCV_2018_paper | Fictitious GAN: Training GANs with Historical Models | [
"Hao Ge",
"Yin Xia",
"Xu Chen",
"Randall Berry",
"Ying Wu"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yin_Xia_Fictitious_GAN_Training_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yin_Xia_Fictitious_GAN_Training_ECCV_2018_paper.pdf | null | null | 1803.08647 | title_snapshot | Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the... |
Anirudh_Som_Perturbation_Robust_Representations_ECCV_2018_paper | Perturbation Robust Representations of Topological Persistence Diagrams | [
"Anirudh Som",
"Kowshik Thopalli",
"Karthikeyan Natesan Ramamurthy",
"Vinay Venkataraman",
"Ankita Shukla",
"Pavan Turaga"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Anirudh_Som_Perturbation_Robust_Representations_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Anirudh_Som_Perturbation_Robust_Representations_ECCV_2018_paper.pdf | null | null | 1807.10400 | title_snapshot | Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision, including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attribute... |
Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper | DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures | [
"Jin-Dong Dong",
"An-Chieh Cheng",
"Da-Cheng Juan",
"Wei Wei",
"Min Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.pdf | null | null | 1806.08198 | title_snapshot | Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, these techniques typically ignore device-related objectives such as inference time, memory usage, and power consumption. Optimizing neural ar... |
Changqing_Zou_SketchyScene_Richly-Annotated_Scene_ECCV_2018_paper | SketchyScene: Richly-Annotated Scene Sketches | [
"Changqing Zou",
"Qian Yu",
"Ruofei Du",
"Haoran Mo",
"Yi-Zhe Song",
"Tao Xiang",
"Chengying Gao",
"Baoquan Chen",
"Hao Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Changqing_Zou_SketchyScene_Richly-Annotated_Scene_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Changqing_Zou_SketchyScene_Richly-Annotated_Scene_ECCV_2018_paper.pdf | null | null | 1808.02473 | title_snapshot | We contribute the rst large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to eciently generate large quantities realistic ... |
Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper | Contour Knowledge Transfer for Salient Object Detection | [
"Xin Li",
"Fan Yang",
"Hong Cheng",
"Wei Liu",
"Dinggang Shen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf | null | null | null | null | In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in salient object detection. However, training such a deep model requires a large amount of manual annotations. Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient o... |
Heng_Wang_Scenes-Objects-Actions_A_Multi-Task_ECCV_2018_paper | Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset | [
"Jamie Ray",
"Heng Wang",
"Du Tran",
"Yufei Wang",
"Matt Feiszli",
"Lorenzo Torresani",
"Manohar Paluri"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Heng_Wang_Scenes-Objects-Actions_A_Multi-Task_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Heng_Wang_Scenes-Objects-Actions_A_Multi-Task_ECCV_2018_paper.pdf | null | null | null | null | This paper introduces a large-scale, multi-label and multitask video dataset named Scenes-Objects-Actions (SOA). Most prior video datasets are based on a predened taxonomy, which is used to de- ne the keyword queries issued to search engines. The videos retrieved by the search engines are then veried for correctness by... |
Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper | Saliency Detection in 360° Videos | [
"Ziheng Zhang",
"Yanyu Xu",
"Jingyi Yu",
"Shenghua Gao"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.pdf | null | null | null | null | This paper presents a novel spherical convolutional neural network based scheme for saliency detection for 360° videos. Specifically, in our spherical convolution neural network definition, kernel is defined on a spherical crown, and the convolution involves the rotation of the kernel along the sphere. Considering that... |
Zeming_Li_DetNet_Design_Backbone_ECCV_2018_paper | DetNet: Design Backbone for Object Detection | [
"Zeming Li",
"Chao Peng",
"Gang Yu",
"Xiangyu Zhang",
"Yangdong Deng",
"Jian Sun"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zeming_Li_DetNet_Design_Backbone_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zeming_Li_DetNet_Design_Backbone_ECCV_2018_paper.pdf | null | null | null | null | Recent CNN based object detectors, either one-stage methods like YOLO, SSD, and RetinaNet, or two-stage detectors like Faster R-CNN, R-FCN and FPN, are usually trying to directly finetune from ImageNet pre-trained models designed for the task of image classification. However, there has been little work discussing the b... |
Seong_Tae_Kim_Facial_Dynamics_Interpreter_ECCV_2018_paper | Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation? | [
"Seong Tae Kim",
"Yong Man Ro"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Seong_Tae_Kim_Facial_Dynamics_Interpreter_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Seong_Tae_Kim_Facial_Dynamics_Interpreter_ECCV_2018_paper.pdf | null | null | 1711.10688 | title_snapshot | Human face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. The motion of a facial local region in facial expression is related to the motion of other facial local regions. In this paper, a novel deep learning a... |
Hai_Ci_Video_Object_Segmentation_ECCV_2018_paper | Video Object Segmentation by Learning Location-Sensitive Embeddings | [
"Hai Ci",
"Chunyu Wang",
"Yizhou Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Hai_Ci_Video_Object_Segmentation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Hai_Ci_Video_Object_Segmentation_ECCV_2018_paper.pdf | null | null | null | null | We address the problem of video object segmentation which outputs the masks of a target object throughout a video given only a bounding box in the first frame. There are two main challenges for this task. First, the background may contain similar objects as the target. Second, the appearance of the target object may ch... |
Bruce_Hou_Transferable_Adversarial_Perturbations_ECCV_2018_paper | Transferable Adversarial Perturbations | [
"Wen Zhou",
"Xin Hou",
"Yongjun Chen",
"Mengyun Tang",
"Xiangqi Huang",
"Xiang Gan",
"Yong Yang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Bruce_Hou_Transferable_Adversarial_Perturbations_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Bruce_Hou_Transferable_Adversarial_Perturbations_ECCV_2018_paper.pdf | null | null | null | null | State-of-the-art deep neural network classifiers are highly vulnerable to adversarial examples which are designed to mislead classifiers with a very small perturbation. However, the performance of black-box attacks (without knowledge of the model parameters) against deployed models always degrades significantly. In thi... |
Zhiwen_Fan_A_Segmentation-aware_Deep_ECCV_2018_paper | A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI | [
"Zhiwen Fan",
"Liyan Sun",
"Xinghao Ding",
"Yue Huang",
"Congbo Cai",
"John Paisley"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Zhiwen_Fan_A_Segmentation-aware_Deep_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Zhiwen_Fan_A_Segmentation-aware_Deep_ECCV_2018_paper.pdf | null | null | 1804.01210 | title_snapshot | Compressed sensing MRI is a classic inverse problem in the field of computational imaging, accelerating the MR imaging by measuring less k-space data. The deep neural network models provide the stronger representation ability and faster reconstruction compared with "shallow" optimization-based methods. However, in the ... |
Albert_Pumarola_Anatomically_Coherent_Facial_ECCV_2018_paper | GANimation: Anatomically-aware Facial Animation from a Single Image | [
"Albert Pumarola",
"Antonio Agudo",
"Aleix M. Martinez",
"Alberto Sanfeliu",
"Francesc Moreno-Noguer"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Albert_Pumarola_Anatomically_Coherent_Facial_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Albert_Pumarola_Anatomically_Coherent_Facial_ECCV_2018_paper.pdf | null | null | 1807.09251 | title_snapshot | Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that conditions GANs' generation process with images of a specific domain, namely a set of images of persons sharing the same expression. While ef... |
Jianwei_Yang_Graph_R-CNN_for_ECCV_2018_paper | Graph R-CNN for Scene Graph Generation | [
"Jianwei Yang",
"Jiasen Lu",
"Stefan Lee",
"Dhruv Batra",
"Devi Parikh"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Jianwei_Yang_Graph_R-CNN_for_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Jianwei_Yang_Graph_R-CNN_for_ECCV_2018_paper.pdf | null | null | 1808.00191 | title_snapshot | We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also ... |
Antonio_Torralba_Interpretable_Basis_Decomposition_ECCV_2018_paper | Interpretable Basis Decomposition for Visual Explanation | [
"Bolei Zhou",
"Yiyou Sun",
"David Bau",
"Antonio Torralba"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Antonio_Torralba_Interpretable_Basis_Decomposition_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Antonio_Torralba_Interpretable_Basis_Decomposition_ECCV_2018_paper.pdf | null | null | null | null | Explanations of the decisions made by a deep neural network are important for human end-users to be able to understand and diagnose the trustworthiness of the system. Current neural networks used for visual recognition are generally used as black boxes that do not provide any human interpretable justification for a pre... |
Nikolaos_Karianakis_Reinforced_Temporal_Attention_ECCV_2018_paper | Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification | [
"Nikolaos Karianakis",
"Zicheng Liu",
"Yinpeng Chen",
"Stefano Soatto"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Nikolaos_Karianakis_Reinforced_Temporal_Attention_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Nikolaos_Karianakis_Reinforced_Temporal_Attention_ECCV_2018_paper.pdf | null | null | 1705.09882 | title_snapshot | We address the problem of person re-identification from commodity depth sensors. One challenge for depth-based recognition is data scarcity. Our first contribution addresses this problem by introducing split-rate RGB-to-Depth transfer, which leverages large RGB datasets more effectively than popular fine-tuning approac... |
Chao_Li_ArticulatedFusion_Real-time_Reconstruction_ECCV_2018_paper | ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera | [
"Chao Li",
"Zheheng Zhao",
"Xiaohu Guo"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Chao_Li_ArticulatedFusion_Real-time_Reconstruction_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Chao_Li_ArticulatedFusion_Real-time_Reconstruction_ECCV_2018_paper.pdf | null | null | 1807.07243 | title_snapshot | This paper proposes a real-time dynamic scene reconstruction method capable of reproducing the motion, geometry, and segmentation simultaneously given live depth stream from a single RGB-D camera. Our approach fuses geometry frame by frame and uses a segmentation-enhanced node graph structure to drive the deformation o... |
Ge_Deep_Metric_Learning_ECCV_2018_paper | Deep Metric Learning with Hierarchical Triplet Loss | [
"Weifeng Ge"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ge_Deep_Metric_Learning_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ge_Deep_Metric_Learning_ECCV_2018_paper.pdf | null | null | 1810.06951 | title_snapshot | We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global context information. This allows us to cope with the main limitation of random sampling in training a conventional triplet loss, which is a c... |
Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper | Deep Directional Statistics: Pose Estimation with Uncertainty Quantification | [
"Sergey Prokudin",
"Peter Gehler",
"Sebastian Nowozin"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper.pdf | null | null | 1805.03430 | title_snapshot | Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.... |
Carl_Toft_Semantic_Match_Consistency_ECCV_2018_paper | Semantic Match Consistency for Long-Term Visual Localization | [
"Carl Toft",
"Erik Stenborg",
"Lars Hammarstrand",
"Lucas Brynte",
"Marc Pollefeys",
"Torsten Sattler",
"Fredrik Kahl"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Carl_Toft_Semantic_Match_Consistency_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Carl_Toft_Semantic_Match_Consistency_ECCV_2018_paper.pdf | null | null | null | null | Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional feature-based methods often struggle in these conditi... |
Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper | Decouple Learning for Parameterized Image Operators | [
"Qingnan Fan",
"Dongdong Chen",
"Lu Yuan",
"Gang Hua",
"Nenghai Yu",
"Baoquan Chen"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper.pdf | null | null | 1807.08186 | title_snapshot | Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameteri... |
Safa_Messaoud_Structural_Consistency_and_ECCV_2018_paper | Structural Consistency and Controllability for Diverse Colorization | [
"Safa Messaoud",
"David Forsyth",
"Alexander G. Schwing"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Safa_Messaoud_Structural_Consistency_and_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Safa_Messaoud_Structural_Consistency_and_ECCV_2018_paper.pdf | null | null | 1809.02129 | title_snapshot | Colorizing a given gray-level image is an important task in the media and advertising industry. Due to the ambiguity inherent to colorization (many shades are often plausible), recent approaches started to explicitly model diversity. However, one of the most obvious artifacts, structural inconsistency, is rarely consid... |
Calvin_Murdock_Deep_Component_Analysis_ECCV_2018_paper | Deep Component Analysis via Alternating Direction Neural Networks | [
"Calvin Murdock",
"MingFang Chang",
"Simon Lucey"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Calvin_Murdock_Deep_Component_Analysis_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Calvin_Murdock_Deep_Component_Analysis_ECCV_2018_paper.pdf | null | null | 1803.06407 | title_snapshot | Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich intuition and theory, but smaller capacity often limits its usefulness. To bridge t... |
T_M_Feroz_Ali_Maximum_Margin_Metric_ECCV_2018_paper | Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification | [
"T M Feroz Ali",
"Subhasis Chaudhuri"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/T_M_Feroz_Ali_Maximum_Margin_Metric_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/T_M_Feroz_Ali_Maximum_Margin_Metric_ECCV_2018_paper.pdf | null | null | 1807.10908 | title_snapshot | In this paper we propose a novel metric learning framework called Nullspace Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the small sample size (SSS) problem inherent in person re-identification and offers a significant performance gain over existing state-of-the-art methods. Taking advantag... |
Xuelin_Qian_Pose-Normalized_Image_Generation_ECCV_2018_paper | Pose-Normalized Image Generation for Person Re-identification | [
"Xuelin Qian",
"Yanwei Fu",
"Tao Xiang",
"Wenxuan Wang",
"Jie Qiu",
"Yang Wu",
"Yu-Gang Jiang",
"Xiangyang Xue"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Xuelin_Qian_Pose-Normalized_Image_Generation_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Xuelin_Qian_Pose-Normalized_Image_Generation_ECCV_2018_paper.pdf | null | null | 1712.02225 | title_snapshot | Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for... |
Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper | Cross-Modal Hamming Hashing | [
"Yue Cao",
"Bin Liu",
"Mingsheng Long",
"Jianmin Wang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper.pdf | null | null | null | null | Cross-modal hashing enables similarity retrieval across different content modalities, such as searching relevant images in response to text queries. It provides with the advantages of computation efficiency and retrieval quality for multimedia retrieval. Hamming space retrieval enables efficient constant-time search th... |
NIKITA_DVORNIK_Modeling_Visual_Context_ECCV_2018_paper | Modeling Visual Context is Key to Augmenting Object Detection Datasets | [
"Nikita Dvornik",
"Julien Mairal",
"Cordelia Schmid"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/NIKITA_DVORNIK_Modeling_Visual_Context_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/NIKITA_DVORNIK_Modeling_Visual_Context_ECCV_2018_paper.pdf | null | null | 1807.07428 | title_snapshot | Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems. By artificially increasing the number of training examples, it helps reducing overfitting and improves generalization. For object detection, classical approaches for data augmentation co... |
Wayne_Wu_Learning_to_Reenact_ECCV_2018_paper | ReenactGAN: Learning to Reenact Faces via Boundary Transfer | [
"Wayne Wu",
"Yunxuan Zhang",
"Cheng Li",
"Chen Qian",
"Chen Change Loy"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Wayne_Wu_Learning_to_Reenact_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Wayne_Wu_Learning_to_Reenact_ECCV_2018_paper.pdf | null | null | 1807.11079 | title_snapshot | We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from an arbitrary person’s monocular video input to a target person’s video. Instead of performing a direct transfer in the pixel space, which could res... |
Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper | Universal Sketch Perceptual Grouping | [
"Ke Li",
"Kaiyue Pang",
"Jifei Song",
"Yi-Zhe Song",
"Tao Xiang",
"Timothy M. Hospedales",
"Honggang Zhang"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.pdf | null | null | null | null | In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To ov... |
Keizo_Kato_Compositional_Learning_of_ECCV_2018_paper | Compositional Learning for Human Object Interaction | [
"Keizo Kato",
"Yin Li",
"Abhinav Gupta"
] | https://www.ecva.net/papers/eccv_2018/papers_ECCV/html/Keizo_Kato_Compositional_Learning_of_ECCV_2018_paper.php | https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Keizo_Kato_Compositional_Learning_of_ECCV_2018_paper.pdf | null | null | null | null | The world of human-object interactions is rich. While generally we sit on chairs and sofas, if need be we can even sit on TVs or top of shelves. In recent years, there has been progress in modeling actions and human-object interactions. However, most of these approaches require lots of data. It is not clear if the lear... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.