paper_id string | title string | authors list | ecva_url string | pdf_url string | supp_url string | doi string | arxiv_id string | arxiv_id_source string | abstract large_string |
|---|---|---|---|---|---|---|---|---|---|
6101_ECCV_2020_paper | Learning to Localize Actions from Moments | [
"Fuchen Long",
"Ting Yao",
"Zhaofan Qiu",
"Xinmei Tian",
"Jiebo Luo",
"Tao Mei"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6101_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480137.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480137-supp.zip | 10.1007/978-3-030-58580-8_9 | 2008.13705 | title_snapshot | With the knowledge of action moments (i.e., trimmed video clips that each contains an action instance), humans could routinely localize an action temporally in an untrimmed video. Nevertheless, most practical methods still require all training videos to be labeled with temporal annotations (action category and temporal... |
6147_ECCV_2020_paper | ForkGAN: Seeing into the Rainy Night | [
"Ziqiang Zheng",
"Yang Wu",
"Xinran Han",
"Jianbo Shi"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6147_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480154.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480154-supp.zip | 10.1007/978-3-030-58580-8_10 | null | null | We present a ForkGAN for task-agnostic image translation that can boost multiple vision tasks in adverse weather conditions. Three tasks of image localization/retrieval, semantic image segmentation, and object detection are evaluated. The key challenge is achieving high-quality image translation without any explicit su... |
6209_ECCV_2020_paper | TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning | [
"Xinwei Sun",
"Yilun Xu",
"Peng Cao",
"Yuqing Kong",
"Lingjing Hu",
"Shanghang Zhang",
"Yizhou Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6209_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480171.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480171-supp.zip | 10.1007/978-3-030-58580-8_11 | 2007.06793 | title_snapshot | Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality with a large amount of data, which leads to a crucial problem of such semi-supervised multi-modal learning. Existing methods suffer from eith... |
6502_ECCV_2020_paper | ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval | [
"Quan Cui",
"Qing-Yuan Jiang",
"Xiu-Shen Wei",
"Wu-Jun Li",
"Osamu Yoshie"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6502_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480188.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480188-supp.pdf | 10.1007/978-3-030-58580-8_12 | 2008.01369 | title_snapshot | Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects. In this paper, we study the novel fine-g... |
22_ECCV_2020_paper | TSIT: A Simple and Versatile Framework for Image-to-Image Translation | [
"Liming Jiang",
"Changxu Zhang",
"Mingyang Huang",
"Chunxiao Liu",
"Jianping Shi",
"Chen Change Loy"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/22_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480205.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480205-supp.pdf | 10.1007/978-3-030-58580-8_13 | 2007.12072 | title_snapshot | We introduce a simple and versatile framework for image-to-image translation. We unearth the importance of normalization layers, and provide a carefully designed two-stream generative model with newly proposed feature transformations in a coarse-to-fine fashion. This allows multi-scale semantic structure information an... |
43_ECCV_2020_paper | ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices | [
"Xiangyu He",
"Zitao Mo",
"Ke Cheng",
"Weixiang Xu",
"Qinghao Hu",
"Peisong Wang",
"Qingshan Liu",
"Jian Cheng"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/43_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480222.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480222-supp.pdf | 10.1007/978-3-030-58580-8_14 | null | null | Training Binarized Neural Networks (BNNs) is challenging due to the discreteness. In order to efficiently optimize BNNs through backward propagations, real-valued auxiliary variables are commonly used to accumulate gradient updates. Those auxiliary variables are then directly quantized to binary weights in the forward ... |
148_ECCV_2020_paper | HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation | [
"Can Wang",
"Jiefeng Li",
"Wentao Liu",
"Chen Qian",
"Cewu Lu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/148_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480256.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480256-supp.zip | 10.1007/978-3-030-58580-8_15 | 2008.00206 | title_snapshot | Remarkable progress has been made in 3D human pose estimation from a monocular RGB camera. However, only a few studies explored 3D multi-person cases. In this paper, we attempt to address the lack of a global perspective of the top-down approaches by introducing a novel form of supervision - Hierarchical Multi-person O... |
193_ECCV_2020_paper | Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve | [
"Weicheng Kuo",
"Anelia Angelova",
"Tsung-Yi Lin",
"Angela Dai"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/193_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480273.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480273-supp.pdf | 10.1007/978-3-030-58580-8_16 | 2007.13034 | title_snapshot | Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses. ... |
223_ECCV_2020_paper | A Unified Framework of Surrogate Loss by Refactoring and Interpolation | [
"Lanlan Liu",
"Mingzhe Wang",
"Jia Deng"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/223_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480290.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480290-supp.pdf | 10.1007/978-3-030-58580-8_17 | 2007.13870 | title_snapshot | We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surrogate losses. Our key observation is that in many cases, evaluating a model with a performance metric on a batch of examples can be refactore... |
362_ECCV_2020_paper | Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images | [
"Sai Bi",
"Zexiang Xu",
"Kalyan Sunkavalli",
"Miloš Hašan",
"Yannick Hold-Geoffroy",
"David Kriegman",
"Ravi Ramamoorthi"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/362_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480307.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480307-supp.zip | 10.1007/978-3-030-58580-8_18 | 2007.09892 | title_snapshot | We present a deep learning approach to reconstruct scene appearance from unstructured images captured under collocated point lighting. At the heart of Deep Reflectance Volumes is a novel volumetric scene representation consisting of opacity, surface normal and reflectance voxel grids. We present a novel physically-base... |
366_ECCV_2020_paper | Memory-augmented Dense Predictive Coding for Video Representation Learning | [
"Tengda Han",
"Weidi Xie",
"Andrew Zisserman"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/366_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480324.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480324-supp.pdf | 10.1007/978-3-030-58580-8_19 | 2008.01065 | title_snapshot | The objective of this paper is self-supervised learning from video, in particular for representations for action recognition. We make the following contributions: (i) We propose a new architecture and learning framework Memory-augmented Dense Predictive Coding (MemDPC) for the task. It is trained with a predictive atte... |
378_ECCV_2020_paper | PointMixup: Augmentation for Point Clouds | [
"Yunlu Chen",
"Vincent Tao Hu",
"Efstratios Gavves",
"Thomas Mensink",
"Pascal Mettes",
"Pengwan Yang",
"Cees G. M. Snoek"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/378_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480341.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480341-supp.pdf | 10.1007/978-3-030-58580-8_20 | 2008.06374 | title_snapshot | This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the... |
415_ECCV_2020_paper | Identity-Guided Human Semantic Parsing for Person Re-Identification | [
"Kuan Zhu",
"Haiyun Guo",
"Zhiwei Liu",
"Ming Tang",
"Jinqiao Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/415_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480358.pdf | null | 10.1007/978-3-030-58580-8_21 | 2007.13467 | title_snapshot | Existing alignment-based methods have to employ the pre-trained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identity-guided human semantic parsing approach (ISP) to ... |
462_ECCV_2020_paper | Learning Gradient Fields for Shape Generation | [
"Ruojin Cai",
"Guandao Yang",
"Hadar Averbuch-Elor",
"Zekun Hao",
"Serge Belongie",
"Noah Snavely",
"Bharath Hariharan"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/462_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480375.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480375-supp.pdf | 10.1007/978-3-030-58580-8_22 | 2008.06520 | title_snapshot | In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. Point cloud generation thus amounts to moving randomly sampled points to high-density areas. We genera... |
467_ECCV_2020_paper | COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder | [
"Kuniaki Saito",
"Kate Saenko",
"Ming-Yu Liu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/467_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480392.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480392-supp.pdf | 10.1007/978-3-030-58580-8_23 | 2007.07431 | title_snapshot | Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping. Few-shot unsupervised image-to-image translation further attempts to generalize the model to an unseen domain by leveraging example i... |
492_ECCV_2020_paper | Corner Proposal Network for Anchor-free, Two-stage Object Detection | [
"Kaiwen Duan",
"Lingxi Xie",
"Honggang Qi",
"Song Bai",
"Qingming Huang",
"Qi Tian"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/492_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480409.pdf | null | 10.1007/978-3-030-58580-8_24 | 2007.13816 | title_snapshot | Two-stage Object Detection","The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each... |
495_ECCV_2020_paper | PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click | [
"Henghui Ding",
"Scott Cohen",
"Brian Price",
"Xudong Jiang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/495_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480426.pdf | null | 10.1007/978-3-030-58580-8_25 | null | null | Existing interactive object segmentation methods mainly take spatial interactions such as bounding boxes or clicks as input. However, these interactions do not contain information about explicit attributes of the target-of-interest and thus cannot quickly specify what the selected object exactly is, especially when the... |
513_ECCV_2020_paper | Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing | [
"Yapeng Tian",
"Dingzeyu Li",
"Chenliang Xu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/513_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480443.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480443-supp.zip | 10.1007/978-3-030-58580-8_26 | 2007.10558 | title_snapshot | In this paper, we introduce a new problem, named audio-visual video parsing, which aims to parse a video into temporal event segments and label them as either audible, visible, or both. Such a problem is essential for a complete understanding of the scene depicted inside a video. To facilitate exploration, we collect a... |
526_ECCV_2020_paper | Learning Delicate Local Representations for Multi-Person Pose Estimation | [
"Yuanhao Cai",
"Zhicheng Wang",
"Zhengxiong Luo",
"Binyi Yin",
"Angang Du",
"Haoqian Wang",
"Xiangyu Zhang",
"Xinyu Zhou",
"Erjin Zhou",
"Jian Sun"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/526_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480460.pdf | null | 10.1007/978-3-030-58580-8_27 | 2003.04030 | title_snapshot | In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delicate local representations, which retain rich low-level spatial information and result in precise keypoint localization. Additionally, we obse... |
544_ECCV_2020_paper | Learning to Plan with Uncertain Topological Maps | [
"Edward Beeching",
"Jilles Dibangoye",
"Olivier Simonin",
"Christian Wolf"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/544_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480477.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480477-supp.zip | 10.1007/978-3-030-58580-8_28 | 2007.05270 | title_snapshot | We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a... |
574_ECCV_2020_paper | Neural Design Network: Graphic Layout Generation with Constraints | [
"Hsin-Ying Lee",
"Lu Jiang",
"Irfan Essa",
"Phuong B Le",
"Haifeng Gong",
"Ming-Hsuan Yang",
"Weilong Yang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/574_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480494.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480494-supp.pdf | 10.1007/978-3-030-58580-8_29 | 1912.09421 | title_snapshot | Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation tha... |
591_ECCV_2020_paper | Learning Open Set Network with Discriminative Reciprocal Points | [
"Guangyao Chen",
"Limeng Qiao",
"Yemin Shi",
"Peixi Peng",
"Jia Li",
"Tiejun Huang",
"Shiliang Pu",
"Yonghong Tian"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/591_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480511.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480511-supp.pdf | 10.1007/978-3-030-58580-8_30 | 2011.00178 | title_snapshot | Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as 'unknown'. In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount... |
597_ECCV_2020_paper | Convolutional Occupancy Networks | [
"Songyou Peng",
"Michael Niemeyer",
"Lars Mescheder",
"Marc Pollefeys",
"Andreas Geiger"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/597_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480528.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480528-supp.pdf | 10.1007/978-3-030-58580-8_31 | 2003.04618 | title_snapshot | Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implic... |
672_ECCV_2020_paper | Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry | [
"He Chen",
"Pengfei Guo",
"Pengfei Li",
"Gim Hee Lee",
"Gregory Chirikjian"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/672_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480545.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480545-supp.pdf | 10.1007/978-3-030-58580-8_32 | 2007.10986 | title_snapshot | Epipolar constraints are at the core of feature matching and depth estimation in current multi-person multi-camera 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances mainly due to... |
849_ECCV_2020_paper | TIDE: A General Toolbox for Identifying Object Detection Errors | [
"Daniel Bolya",
"Sean Foley",
"James Hays",
"Judy Hoffman"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/849_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480562.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480562-supp.zip | 10.1007/978-3-030-58580-8_33 | 2008.08115 | title_snapshot | We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction syste... |
893_ECCV_2020_paper | PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding | [
"Saining Xie",
"Jiatao Gu",
"Demi Guo",
"Charles R. Qi",
"Leonidas Guibas",
"Or Litany"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/893_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480579.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480579-supp.pdf | 10.1007/978-3-030-58580-8_34 | 2007.10985 | title_snapshot | Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (g, ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little ... |
922_ECCV_2020_paper | DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation | [
"Xuefei Ning",
"Tianchen Zhao",
"Wenshuo Li",
"Peng Lei",
"Yu Wang",
"Huazhong Yang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/922_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480596.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480596-supp.pdf | 10.1007/978-3-030-58580-8_35 | 2004.02164 | title_snapshot | Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we prop... |
990_ECCV_2020_paper | Circumventing Outliers of AutoAugment with Knowledge Distillation | [
"Longhui Wei",
"An Xiao",
"Lingxi Xie",
"Xiaopeng Zhang",
"Xin Chen",
"Qi Tian"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/990_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480613.pdf | null | 10.1007/978-3-030-58580-8_36 | 2003.11342 | title_snapshot | AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part o... |
997_ECCV_2020_paper | S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching | [
"Hugo Germain",
"Guillaume Bourmaud",
"Vincent Lepetit"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/997_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480630.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480630-supp.pdf | 10.1007/978-3-030-58580-8_37 | 2004.01673 | title_judge | Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, ... |
1054_ECCV_2020_paper | RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving | [
"Peixuan Li",
"Huaici Zhao",
"Pengfei Liu",
"Feidao Cao"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1054_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480647.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480647-supp.zip | 10.1007/978-3-030-58580-8_38 | 2001.03343 | title_snapshot | In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatic... |
1062_ECCV_2020_paper | Video Object Segmentation with Episodic Graph Memory Networks | [
"Xiankai Lu",
"Wenguan Wang",
"Martin Danelljan",
"Tianfei Zhou",
"Jianbing Shen",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1062_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480664.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480664-supp.pdf | 10.1007/978-3-030-58580-8_39 | 2007.07020 | title_snapshot | How to make a segmentation model efficiently adapt to a specific video as well as online target appearance variations is a fun- damental issue in the field of video object segmentation. In this work, a graph memory network is developed to address the novel idea of “learning to update the segmentation model”. Specifical... |
1101_ECCV_2020_paper | Rethinking Bottleneck Structure for Efficient Mobile Network Design | [
"Daquan Zhou",
"Qibin Hou",
"Yunpeng Chen",
"Jiashi Feng",
"Shuicheng Yan"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1101_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480681.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480681-supp.pdf | 10.1007/978-3-030-58580-8_40 | 2007.02269 | title_snapshot | The inverted residual block is dominating architecture design for mobile networks recently. It changes the classic residual bottleneck by introducing two design rules: learning inverted residuals and using linear bottlenecks. In this paper, we rethink the necessity of such design change and find it may bring risks of i... |
1104_ECCV_2020_paper | Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks | [
"Jeffrey O. Zhang",
"Alexander Sax",
"Amir Zamir",
"Leonidas Guibas",
"Jitendra Malik"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1104_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480698.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480698-supp.pdf | 10.1007/978-3-030-58580-8_41 | 1912.13503 | title_snapshot | When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in cases when training data is scarce, when a single learner needs to perform multiple tasks, or when one wishes to encode priors in the netwo... |
1121_ECCV_2020_paper | Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach | [
"Zerui Chen",
"Yan Huang",
"Hongyuan Yu",
"Bin Xue",
"Ke Han",
"Yiru Guo",
"Liang Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1121_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480715.pdf | null | 10.1007/978-3-030-58580-8_42 | null | null | Even though most existing monocular 3D pose estimation approaches achieve very competitive results, they ignore the heterogeneity among human body parts by estimating them with the same network architecture. To accurately estimate 3D poses of different body parts, we attempt to build a part-aware 3D pose estimator by s... |
1207_ECCV_2020_paper | REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets | [
"Angelina Wang",
"Arvind Narayanan",
"Olga Russakovsky"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1207_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480732.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480732-supp.pdf | 10.1007/978-3-030-58580-8_43 | 2004.07999 | title_snapshot | Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. To tackle this issue and to enable the preemptive analysis of large-scale dataset, we present our tool. REVISE (REvealing VIs... |
1327_ECCV_2020_paper | Contrastive Learning for Weakly Supervised Phrase Grounding | [
"Tanmay Gupta",
"Arash Vahdat",
"Gal Chechik",
"Xiaodong Yang",
"Jan Kautz",
"Derek Hoiem"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1327_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480749.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480749-supp.zip | 10.1007/978-3-030-58580-8_44 | 2006.09920 | title_snapshot | Phrase grounding, the problem of associating image regions to caption words, is a crucial component of vision-language tasks. We show that phrase grounding can be learned by optimizing word-region attention to maximize a lower bound on mutual information between images and caption words. Given pairs of images and capti... |
1362_ECCV_2020_paper | Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-Order Feature Analysis | [
"Siyuan Yang",
"Jun Liu",
"Shijian Lu",
"Meng Hwa Er",
"Alex C. Kot"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1362_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480766.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480766-supp.pdf | 10.1007/978-3-030-58580-8_45 | null | null | Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both... |
1425_ECCV_2020_paper | Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors | [
"Zuxuan Wu",
"Ser-Nam Lim",
"Larry S. Davis",
"Tom Goldstein"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1425_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490001.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490001-supp.pdf | 10.1007/978-3-030-58548-8_1 | 1910.14667 | title_snapshot | We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effec... |
1449_ECCV_2020_paper | TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images | [
"Jianxin Lin",
"Yingxue Pang",
"Yingce Xia",
"Zhibo Chen",
"Jiebo Luo"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1449_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490018.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490018-supp.pdf | 10.1007/978-3-030-58548-8_2 | 2004.04634 | title_snapshot | An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue... |
1479_ECCV_2020_paper | Semi-Siamese Training for Shallow Face Learning | [
"Hang Du",
"Hailin Shi",
"Yuchi Liu",
"Jun Wang",
"Zhen Lei",
"Dan Zeng",
"Tao Mei"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1479_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490035.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490035-supp.pdf | 10.1007/978-3-030-58548-8_3 | 2007.08398 | title_snapshot | Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) for training. However, in many real-world scenarios of face recognition, the training dataset is limited in depth, $ extit{i.e.}$ only two fac... |
1488_ECCV_2020_paper | GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework | [
"Haotao Wang",
"Shupeng Gui",
"Haichuan Yang",
"Ji Liu",
"Zhangyang Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1488_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490052.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490052-supp.pdf | 10.1007/978-3-030-58548-8_4 | 2008.11062 | title_snapshot | Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices. Similar to other deep models, state-of-the-art GANs also suffer from high parameter complexities. That has recently motivated the ex... |
1526_ECCV_2020_paper | Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition | [
"Yukun Su",
"Guosheng Lin",
"Jinhui Zhu",
"Qingyao Wu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1526_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490069.pdf | null | 10.1007/978-3-030-58548-8_5 | null | null | This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these... |
1530_ECCV_2020_paper | Binarized Neural Network for Single Image Super Resolution | [
"Jingwei Xin",
"Nannan Wang",
"Xinrui Jiang",
"Jie Li",
"Heng Huang",
"Xinbo Gao"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1530_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490086.pdf | null | 10.1007/978-3-030-58548-8_6 | null | null | Lighter model and faster inference are the focus of current single image super-resolution (SISR) research. However, existing methods are still hard to be applied in real-world applications due to the requirement of its heavy computation. Model quantization is an effective way to significantly reduce model size and comp... |
1564_ECCV_2020_paper | Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation | [
"Huiyu Wang",
"Yukun Zhu",
"Bradley Green",
"Hartwig Adam",
"Alan Yuille",
"Liang-Chieh Chen"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1564_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490103.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490103-supp.pdf | 10.1007/978-3-030-58548-8_7 | 2003.07853 | title_snapshot | Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this p... |
1605_ECCV_2020_paper | Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation | [
"Zhipeng Fan",
"Jun Liu",
"Yao Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1605_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490120.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490120-supp.zip | 10.1007/978-3-030-58548-8_8 | null | null | 3D hand pose estimation is an important task for a wide range of real-world applications. Existing works in this domain mainly focus on designing advanced algorithms to achieve high pose estimation accuracy. However, besides accuracy, the computation efficiency that affects the computation speed and power consumption i... |
1624_ECCV_2020_paper | Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | [
"Jinlong Peng",
"Changan Wang",
"Fangbin Wan",
"Yang Wu",
"Yabiao Wang",
"Ying Tai",
"Chengjie Wang",
"Jilin Li",
"Feiyue Huang",
"Yanwei Fu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1624_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490137.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490137-supp.zip | 10.1007/978-3-030-58548-8_9 | 2007.14557 | title_snapshot | Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a ... |
1631_ECCV_2020_paper | Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets | [
"Tong Wu",
"Qingqiu Huang",
"Ziwei Liu",
"Yu Wang",
"Dahua Lin"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1631_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490154.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490154-supp.pdf | 10.1007/978-3-030-58548-8_10 | 2007.09654 | title_snapshot | We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional single-label classification problem, multi-label recognition problems are often more challenging due to two significant issues, namely the co-o... |
1676_ECCV_2020_paper | Hamiltonian Dynamics for Real-World Shape Interpolation | [
"Marvin Eisenberger",
"Daniel Cremers"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1676_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490171.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490171-supp.pdf | 10.1007/978-3-030-58548-8_11 | 2004.05199 | title_snapshot | We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of... |
1694_ECCV_2020_paper | Learning to Scale Multilingual Representations for Vision-Language Tasks | [
"Andrea Burns",
"Donghyun Kim",
"Derry Wijaya",
"Kate Saenko",
"Bryan A. Plummer"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1694_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490188.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490188-supp.pdf | 10.1007/978-3-030-58548-8_12 | 2004.04312 | title_snapshot | Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model ... |
1710_ECCV_2020_paper | Multi-modal Transformer for Video Retrieval | [
"Valentin Gabeur",
"Chen Sun",
"Karteek Alahari",
"Cordelia Schmid"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1710_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490205.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490205-supp.pdf | 10.1007/978-3-030-58548-8_13 | 2007.10639 | title_snapshot | The task of retrieving video content relevant to natural language queries plays a critical role in effectively handling internet-scale datasets. Most of the existing methods for this caption-to-video retrieval problem do not fully exploit cross-modal cues present in video. Furthermore, they aggregate per-frame visual f... |
1761_ECCV_2020_paper | Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution | [
"Yanchun Xie",
"Jimin Xiao",
"Mingjie Sun",
"Chao Yao",
"Kaizhu Huang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1761_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490222.pdf | null | 10.1007/978-3-030-58548-8_14 | null | null | In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Instead, we only intend to transfer the relevant textures from reference images to the output super-reso... |
1802_ECCV_2020_paper | RobustFusion: Human Volumetric Capture with Data-driven Visual Cues using a RGBD Camera | [
"Zhuo Su",
"Lan Xu",
"Zerong Zheng",
"Tao Yu",
"Yebin Liu",
"Lu Fang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1802_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490239.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490239-supp.zip | 10.1007/978-3-030-58548-8_15 | null | null | High-quality and complete 4D reconstruction of human activities is critical for immersive VR/AR experience, but it suffers from inherent self-scanning constraint and consequent fragile tracking under the monocular setting. In this paper, inspired by the huge potential of learning-based human modeling, we propose Robust... |
1886_ECCV_2020_paper | Surface Normal Estimation of Tilted Images via Spatial Rectifier | [
"Tien Do",
"Khiem Vuong",
"Stergios I. Roumeliotis",
"Hyun Soo Park"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1886_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490256.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490256-supp.zip | 10.1007/978-3-030-58548-8_16 | 2007.09264 | title_snapshot | In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily oriented sensors such as body-/robot-mounted cameras. Existing approaches exhibit bounded performance on predicting surface normals because... |
1915_ECCV_2020_paper | Multimodal Shape Completion via Conditional Generative Adversarial Networks | [
"Rundi Wu",
"Xuelin Chen",
"Yixin Zhuang",
"Baoquan Chen"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1915_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490273.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490273-supp.pdf | 10.1007/978-3-030-58548-8_17 | 2003.07717 | title_snapshot | Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ignoring the ambiguity when reasoning the missing geometry. Hence, we pose ... |
1977_ECCV_2020_paper | Generative Sparse Detection Networks for 3D Single-shot Object Detection | [
"JunYoung Gwak",
"Christopher Choy",
"Silvio Savarese"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1977_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490290.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490290-supp.pdf | 10.1007/978-3-030-58548-8_18 | 2006.12356 | title_snapshot | 3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality. Yet, the sparse nature of the 3D data poses unique challenges to this task. Most notably, the observable surface of the 3D point clouds is disjoint from the center of the instan... |
1987_ECCV_2020_paper | Grounded Situation Recognition | [
"Sarah Pratt",
"Mark Yatskar",
"Luca Weihs",
"Ali Farhadi",
"Aniruddha Kembhavi"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1987_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490307.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490307-supp.pdf | 10.1007/978-3-030-58548-8_19 | 2003.12058 | title_snapshot | We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical challenges: identify... |
2019_ECCV_2020_paper | Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos | [
"Shaoxiang Chen",
"Wenhao Jiang",
"Wei Liu",
"Yu-Gang Jiang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2019_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490324.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490324-supp.pdf | 10.1007/978-3-030-58548-8_20 | 2007.14164 | title_snapshot | Automatically generating sentences to describe events and temporally localizing sentences in a video are two important tasks that bridge language and videos. Recent techniques leverage the multimodal nature of videos by using off-the-shelf features to represent videos, but interactions between modalities are rarely exp... |
2157_ECCV_2020_paper | Unpaired Learning of Deep Image Denoising | [
"Xiaohe Wu",
"Ming Liu",
"Yue Cao",
"Dongwei Ren",
"Wangmeng Zuo"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2157_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490341.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490341-supp.pdf | 10.1007/978-3-030-58548-8_21 | 2008.13711 | title_snapshot | We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Such problem setting generally is practical and valuable considering that it is feasible to collect unpaired noisy and clean images in most real-world applications. And we further assume that the noise can... |
2191_ECCV_2020_paper | Self-supervising Fine-grained Region Similarities for Large-scale Image Localization | [
"Yixiao Ge",
"Haibo Wang",
"Feng Zhu",
"Rui Zhao",
"Hongsheng Li"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2191_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490358.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490358-supp.pdf | 10.1007/978-3-030-58548-8_22 | 2006.03926 | title_snapshot | The task of large-scale retrieval-based image localization is to estimate the geographical location of a query image by recognizing its nearest reference images from a city-scale dataset. However, the general public benchmarks only provide noisy GPS labels associated with the training images, which act as weak supervis... |
2215_ECCV_2020_paper | Rotationally-Temporally Consistent Novel View Synthesis of Human Performance Video | [
"Youngjoong Kwon",
"Stefano Petrangeli",
"Dahun Kim",
"Haoliang Wang",
"Eunbyung Park",
"Viswanathan Swaminathan",
"Henry Fuchs"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2215_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490375.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490375-supp.zip | 10.1007/978-3-030-58548-8_23 | null | null | Novel view video synthesis aims to synthesize novel viewpoints videos given input captures of a human performance taken from multiple reference viewpoints and over consecutive time steps. Despite great advances in model-free novel view synthesis, existing methods present three limitations when applied to complex and ti... |
2272_ECCV_2020_paper | Side-Aware Boundary Localization for More Precise Object Detection | [
"Jiaqi Wang",
"Wenwei Zhang",
"Yuhang Cao",
"Kai Chen",
"Jiangmiao Pang",
"Tao Gong",
"Jianping Shi",
"Chen Change Loy",
"Dahua Lin"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2272_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490392.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490392-supp.pdf | 10.1007/978-3-030-58548-8_24 | 1912.04260 | title_snapshot | Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placemen... |
2314_ECCV_2020_paper | SF-Net: Single-Frame Supervision for Temporal Action Localization | [
"Fan Ma",
"Linchao Zhu",
"Yi Yang",
"Shengxin Zha",
"Gourab Kundu",
"Matt Feiszli",
"Zheng Shou"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2314_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490409.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490409-supp.zip | 10.1007/978-3-030-58548-8_25 | 2003.06845 | title_snapshot | In this paper, we study an intermediate form of supervision, i.e., single-frame supervision, for temporal action localization (TAL). To obtain the single-frame supervision, the annotators are asked to identify only a single frame within the temporal window of an action. This can significantly reduce the labor cost of o... |
2317_ECCV_2020_paper | Negative Margin Matters: Understanding Margin in Few-shot Classification | [
"Bin Liu",
"Yue Cao",
"Yutong Lin",
"Qi Li",
"Zheng Zhang",
"Mingsheng Long",
"Han Hu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2317_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490426.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490426-supp.pdf | 10.1007/978-3-030-58548-8_26 | 2003.12060 | title_snapshot | In this paper, we unconventionally propose to adopt appropriate negative-margin to softmax loss for few-shot classification, which surprisingly works well for the open-set scenarios of few-shot classification. We then provide the intuitive explanation and the theoretical proof to understand why negative margin works we... |
2323_ECCV_2020_paper | Particularity beyond Commonality: Unpaired Identity Transfer with Multiple References | [
"Ruizheng Wu",
"Xin Tao",
"Yingcong Chen",
"Xiaoyong Shen",
"Jiaya Jia"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2323_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490443.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490443-supp.pdf | 10.1007/978-3-030-58548-8_27 | null | null | Unpaired image-to-image translation aims to translate images from the source class to target one by providing sufficient data for these classes. Current few-shot translation methods use multiple reference images to describe the target domain through extracting common features. In this paper, we focus on a more specific... |
2342_ECCV_2020_paper | Tracking Objects as Points | [
"Xingyi Zhou",
"Vladlen Koltun",
"Philipp Krähenbühl"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2342_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490460.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490460-supp.pdf | 10.1007/978-3-030-58548-8_28 | 2004.01177 | title_snapshot | Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection. In this paper, we present a... |
2390_ECCV_2020_paper | CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis | [
"Jiadong Liang",
"Wenjie Pei",
"Feng Lu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2390_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490477.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490477-supp.pdf | 10.1007/978-3-030-58548-8_29 | 1912.08562 | title_judge | Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. It is fairly arduous due to the cross-modality translation. In this paper we circumvent this problem by focusing on parsing the content of both the input text and the synthesized ima... |
2402_ECCV_2020_paper | Transporting Labels via Hierarchical Optimal Transport for Semi-Supervised Learning | [
"Fariborz Taherkhani",
"Ali Dabouei",
"Sobhan Soleymani",
"Jeremy Dawson",
"Nasser M. Nasrabadi"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2402_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490494.pdf | null | 10.1007/978-3-030-58548-8_30 | null | null | Semi-Supervised Learning (SSL) based on Convolutional Neural Networks (CNNs) have recently been proven as powerful tools for standard tasks such as image classification when there is not a sufficient amount of labeled data available during the training. In this work, we consider the general setting of the SSL problem f... |
2449_ECCV_2020_paper | MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning | [
"Simon Vandenhende",
"Stamatios Georgoulis",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2449_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490511.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490511-supp.pdf | 10.1007/978-3-030-58548-8_31 | 2001.06902 | title_snapshot | In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup. In contrast to common belief, we show that tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales, and vice v... |
2473_ECCV_2020_paper | Learning to Factorize and Relight a City | [
"Andrew Liu",
"Shiry Ginosar",
"Tinghui Zhou",
"Alexei A. Efros",
"Noah Snavely"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2473_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490528.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490528-supp.zip | 10.1007/978-3-030-58548-8_32 | 2008.02796 | title_snapshot | We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors. Inspired by the classic intrinsic image decomposition, our learning signal builds upon two insights: 1) combining the disentangled factors should reconstruct the original image, and 2... |
2495_ECCV_2020_paper | Region Graph Embedding Network for Zero-Shot Learning | [
"Guo-Sen Xie",
"Li Liu",
"Fan Zhu",
"Fang Zhao",
"Zheng Zhang",
"Yazhou Yao",
"Jie Qin",
"Ling Shao"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2495_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490545.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490545-supp.pdf | 10.1007/978-3-030-58548-8_33 | null | null | Most of the existing Zero-Shot Learning (ZSL) approaches learn direct embeddings from global features or image parts (regions) to the semantic space, which, however, fail to capture the appearance relationships between different local regions within a single image. In this paper, to model the relations among local imag... |
2534_ECCV_2020_paper | GRAB: A Dataset of Whole-Body Human Grasping of Objects | [
"Omid Taheri",
"Nima Ghorbani",
"Michael J. Black",
"Dimitrios Tzionas"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2534_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490562.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490562-supp.pdf | 10.1007/978-3-030-58548-8_34 | 2008.11200 | title_snapshot | Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While ""grasping"" is commonly thought of as a single hand stably lifting an object, we capture the mot... |
2616_ECCV_2020_paper | DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects | [
"Edgar Tretschk",
"Ayush Tewari",
"Michael Zollhöfer",
"Vladislav Golyanik",
"Christian Theobalt"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2616_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490579.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490579-supp.zip | 10.1007/978-3-030-58548-8_35 | 1905.10290 | title_snapshot | Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder \hbox{(DEMEA)} which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. The embedded deformation layer (EDL) is a differentiable deformable geo... |
2623_ECCV_2020_paper | RANSAC-Flow: Generic Two-stage Image Alignment | [
"Xi Shen",
"François Darmon",
"Alexei A. Efros",
"Mathieu Aubry"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2623_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490596.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490596-supp.pdf | 10.1007/978-3-030-58548-8_36 | 2004.01526 | title_snapshot | This paper considers the generic problem of dense alignment between two images, whether they be two frames of a video, two widely different views of a scene, two paintings depicting similar content, etc. Whereas each such task is typically addressed with a domain-specific solution, we show that a simple unsupervised ap... |
2632_ECCV_2020_paper | Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds | [
"Arun Balajee Vasudevan",
"Dengxin Dai",
"Luc Van Gool"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2632_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490613.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490613-supp.zip | 10.1007/978-3-030-58548-8_37 | 2003.04210 | title_snapshot | Humans can robustly recognize and localize objects by integrating visual and auditory cues. While machines are able to do the same now with images, less work has been done with sounds. This work develops an approach for dense semantic labelling of sound-making objects, purely based on binaural sounds. We propose a nove... |
2636_ECCV_2020_paper | Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images | [
"Kiru Park",
"Timothy Patten",
"Markus Vincze"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2636_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490630.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490630-supp.zip | 10.1007/978-3-030-58548-8_38 | 2005.03717 | title_snapshot | Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses. However, it is difficult to obtain textured 3D models and annotate the poses of objects in real scenarios. This paper proposes a method, Neural Object Learning (NOL), that create... |
2666_ECCV_2020_paper | Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking | [
"Jianfeng Yan",
"Zizhuang Wei",
"Hongwei Yi",
"Mingyu Ding",
"Runze Zhang",
"Yisong Chen",
"Guoping Wang",
"Yu-Wing Tai"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2666_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490647.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490647-supp.zip | 10.1007/978-3-030-58548-8_39 | 2007.10872 | title_snapshot | In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel hybrid recurrent multi-view stereo net consists of two core modules: 1) a light DRENet (Dense Reception ... |
2707_ECCV_2020_paper | Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application | [
"Xuchong Qiu",
"Yang Xiao",
"Chaohui Wang",
"Renaud Marlet"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2707_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490664.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490664-supp.pdf | 10.1007/978-3-030-58548-8_40 | 2007.12088 | title_snapshot | Inference & Application","We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion da... |
2710_ECCV_2020_paper | MovieNet: A Holistic Dataset for Movie Understanding | [
"Qingqiu Huang",
"Yu Xiong",
"Anyi Rao",
"Jiaze Wang",
"Dahua Lin"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2710_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490681.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490681-supp.pdf | 10.1007/978-3-030-58548-8_41 | 2007.10937 | title_snapshot | Recent years have seen remarkable advances in visual understanding. However, how to understand a story-based long video with artistic styles, e.g. movie, remains challenging. In this paper, we introduce MovieNet -- a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-m... |
2723_ECCV_2020_paper | Short-Term and Long-Term Context Aggregation Network for Video Inpainting | [
"Ang Li",
"Shanshan Zhao",
"Xingjun Ma",
"Mingming Gong",
"Jianzhong Qi",
"Rui Zhang",
"Dacheng Tao",
"Ramamohanarao Kotagiri"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2723_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490698.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490698-supp.zip | 10.1007/978-3-030-58548-8_42 | 2009.05721 | title_snapshot | Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccurate short-term context aggregation or rarely explore long-term frame information. In this work, we present a novel context aggregation netwo... |
2754_ECCV_2020_paper | DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization | [
"Juan Du",
"Rui Wang",
"Daniel Cremers"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2754_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490715.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490715-supp.pdf | 10.1007/978-3-030-58548-8_43 | 2007.09217 | title_snapshot | For relocalization in large-scale point clouds, we propose the first approach that unifies global place recognition and local 6DoF pose refinement. To this end, we design a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points. It integrates FlexConv and Squeeze-and-... |
2755_ECCV_2020_paper | Face Super-Resolution Guided by 3D Facial Priors | [
"Xiaobin Hu",
"Wenqi Ren",
"John LaMaster",
"Xiaochun Cao",
"Xiaoming Li",
"Zechao Li",
"Bjoern Menze",
"Wei Liu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2755_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490732.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490732-supp.pdf | 10.1007/978-3-030-58548-8_44 | 2007.09454 | title_snapshot | State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with f... |
2763_ECCV_2020_paper | Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation | [
"Yabin Zhang",
"Bin Deng",
"Kui Jia",
"Lei Zhang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2763_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490749.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490749-supp.pdf | 10.1007/978-3-030-58548-8_45 | 2007.07695 | title_snapshot | Motivated by the problem relatedness between unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods adopt SSL principles (e.g., the cluster assumption) as their learning ingredients. However, they tend to overlook the very domain-shift nature of UDA. In this work, we ... |
2767_ECCV_2020_paper | Are Labels Necessary for Neural Architecture Search? | [
"Chenxi Liu",
"Piotr Dollár",
"Kaiming He",
"Ross Girshick",
"Alan Yuille",
"Saining Xie"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2767_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490766.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490766-supp.pdf | 10.1007/978-3-030-58548-8_46 | 2003.12056 | title_snapshot | Existing neural network architectures in computer vision --- whether designed by humans or by machines --- were typically found using both images and their associated labels. In this paper, we ask the question: can we find high-quality neural architectures using only images, but no human-annotated labels? To answer thi... |
2776_ECCV_2020_paper | BLSM: A Bone-Level Skinned Model of the Human Mesh | [
"Haoyang Wang",
"Riza Alp Güler",
"Iasonas Kokkinos",
"George Papandreou",
"Stefanos Zafeiriou"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2776_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500001.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500001-supp.zip | 10.1007/978-3-030-58558-7_1 | null | null | We introduce BLSM, a bone-level skinned model of the human body mesh where bone scales are set prior to template synthesis, rather than the common, inverse practice. BLSM first sets bone lengths and joint angles to specify the skeleton, then specifies identity-specific surface variation, and finally bundles them togeth... |
2826_ECCV_2020_paper | Associative Alignment for Few-shot Image Classification | [
"Arman Afrasiyabi",
"Jean-François Lalonde",
"Christian Gagné"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2826_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500018.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500018-supp.pdf | 10.1007/978-3-030-58558-7_2 | 1912.05094 | title_snapshot | Few-shot image classification aims at training a model from only a few examples for each of the ``novel'' classes. This paper proposes the idea of associative alignment for leveraging part of the base data by aligning the novel training instances to the closely related ones in the base training set. This expands the si... |
2873_ECCV_2020_paper | Cyclic Functional Mapping: Self-supervised Correspondence between Non-isometric Deformable Shapes | [
"Dvir Ginzburg",
"Dan Raviv"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2873_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500035.pdf | null | 10.1007/978-3-030-58558-7_3 | 1912.01249 | title_snapshot | We present the first utterly self-supervised network for dense correspondence mapping between non-isometric shapes. The task of alignment in non-Euclidean domains is one of the most fundamental and crucial problems in computer vision. As 3D scanners can generate highly complex and dense models, the mission of finding d... |
2905_ECCV_2020_paper | View-Invariant Probabilistic Embedding for Human Pose | [
"Jennifer J. Sun",
"Jiaping Zhao",
"Liang-Chieh Chen",
"Florian Schroff",
"Hartwig Adam",
"Ting Liu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2905_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500052.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500052-supp.pdf | 10.1007/978-3-030-58558-7_4 | 1912.01001 | title_snapshot | Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in human body poses across multiple views. This ability is useful for analyzing body movements and human behaviors in images and videos. In this... |
2918_ECCV_2020_paper | Contact and Human Dynamics from Monocular Video | [
"Davis Rempe",
"Leonidas J. Guibas",
"Aaron Hertzmann",
"Bryan Russell",
"Ruben Villegas",
"Jimei Yang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2918_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500069.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500069-supp.zip | 10.1007/978-3-030-58558-7_5 | 2007.11678 | title_snapshot | Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles. In this paper, we present a physics-based method for inferring 3D human motion from v... |
2950_ECCV_2020_paper | PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation | [
"Wenxuan Wu",
"Zhi Yuan Wang",
"Zhuwen Li",
"Wei Liu",
"Li Fuxin"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2950_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500086.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500086-supp.zip | 10.1007/978-3-030-58558-7_6 | 1911.12408 | title_judge | We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive... |
2965_ECCV_2020_paper | Points2Surf Learning Implicit Surfaces from Point Clouds | [
"Philipp Erler",
"Paul Guerrero",
"Stefan Ohrhallinger",
"Niloy J. Mitra",
"Michael Wimmer"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2965_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500103.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500103-supp.pdf | 10.1007/978-3-030-58558-7_7 | null | null | A key step in any scanning-based asset creation workflow is to convert unordered point clouds to a surface. Classical methods (e.g. Poisson reconstruction) start to degrade in the presence of noisy and partial scans. Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from ... |
2983_ECCV_2020_paper | Few-Shot Scene-Adaptive Anomaly Detection | [
"Yiwei Lu",
"Frank Yu",
"Mahesh Kumar Krishna Reddy",
"Yang Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2983_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500120.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500120-supp.pdf | 10.1007/978-3-030-58558-7_8 | 2007.07843 | title_snapshot | We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a targe... |
2986_ECCV_2020_paper | Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting | [
"Bindita Chaudhuri",
"Noranart Vesdapunt",
"Linda Shapiro",
"Baoyuan Wang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2986_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500137.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500137-supp.zip | 10.1007/978-3-030-58558-7_9 | 2007.06759 | title_snapshot | Traditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data. Use of deformation transfer or multilinear tensor as a personalized 3DMM for blendshape interpolation... |
2988_ECCV_2020_paper | Entropy Minimisation Framework for Event-based Vision Model Estimation | [
"Urbano Miguel Nunes",
"Yiannis Demiris"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2988_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500154.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500154-supp.pdf | 10.1007/978-3-030-58558-7_10 | null | null | We propose a novel EMin framework for event-based vision model estimation. The framework extends previous event-based motion compensation algorithms to handle models whose outputs have arbitrary dimensions. The main motivation comes from estimating motion from events directly in 3D space (e. g. events augmented with de... |
2992_ECCV_2020_paper | Reconstructing NBA Players | [
"Luyang Zhu",
"Konstantinos Rematas",
"Brian Curless",
"Steven M. Seitz",
"Ira Kemelmacher-Shlizerman"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2992_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500171.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500171-supp.pdf | 10.1007/978-3-030-58558-7_11 | 2007.13303 | title_snapshot | Great progress has been made in 3D body pose and shape estimation from single photos. Yet, state-of-the-art results still suffer from errors due to challenging body poses, modeling clothing, and self occlusions. The domain of basketball games is particularly challenging, due to all of these factors. In this paper, we i... |
3087_ECCV_2020_paper | PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments | [
"Zhiming Chen",
"Kean Chen",
"Weiyao Lin",
"John See",
"Hui Yu",
"Yan Ke",
"Cong Yang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3087_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500188.pdf | null | 10.1007/978-3-030-58558-7_12 | 2007.09584 | title_snapshot | Object detection using an oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional angle dimension optimized by a distance loss. However, as the distance loss on... |
3089_ECCV_2020_paper | TENet: Triple Excitation Network for Video Salient Object Detection | [
"Sucheng Ren",
"Chu Han",
"Xin Yang",
"Guoqiang Han",
"Shengfeng He"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3089_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500205.pdf | null | 10.1007/978-3-030-58558-7_13 | 2007.09943 | title_snapshot | In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to re... |
3099_ECCV_2020_paper | Deep Feedback Inverse Problem Solver | [
"Wei-Chiu Ma",
"Shenlong Wang",
"Jiayuan Gu",
"Sivabalan Manivasagam",
"Antonio Torralba",
"Raquel Urtasun"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3099_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500222.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500222-supp.pdf | 10.1007/978-3-030-58558-7_14 | 2101.07719 | title_snapshot | We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, in each iteration, the neural network takes the feedback as input and outputs an update on current... |
3119_ECCV_2020_paper | Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification | [
"Liuyu Xiang",
"Guiguang Ding",
"Jungong Han"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3119_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500239.pdf | null | 10.1007/978-3-030-58558-7_15 | 2001.01536 | title_snapshot | In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that networks traine... |
3120_ECCV_2020_paper | Hallucinating Visual Instances in Total Absentia | [
"Jiayan Qiu",
"Yiding Yang",
"Xinchao Wang",
"Dacheng Tao"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3120_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500256.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500256-supp.pdf | 10.1007/978-3-030-58558-7_16 | null | null | In this paper, we investigate a new visual restoration task, termed as hallucinating visual instances in total absentia (HVITA). Unlike conventional image inpainting task that works on images with only part of a visual instance missing, HVITA concerns scenarios where an object is completely absent from the scene. This ... |
3125_ECCV_2020_paper | Weakly-supervised 3D Shape Completion in the Wild | [
"Jiayuan Gu",
"Wei-Chiu Ma",
"Sivabalan Manivasagam",
"Wenyuan Zeng",
"Zihao Wang",
"Yuwen Xiong",
"Hao Su",
"Raquel Urtasun"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3125_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500273.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500273-supp.zip | 10.1007/978-3-030-58558-7_17 | 2008.09110 | title_snapshot | 3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from previous methods, we address the problem of learning 3D complete shape from unaligned and real-world partial point clouds. To this end, we pro... |
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