eccv 2020 accepted paper list

For each paper, the Publication Fee needs to be paid by the camera ready deadline. Make sure to use relative paths for referencing files. The virtual conference will consist of a live-stream of the keynotes and oral sessions, with the opportunity for questions and answers with the presenters, as well as poster sessions consisting of pre-recorded videos for all papers as well as interactive discussions with authors over text, audio and video via rocket.chat channels. Image Recognition; 2D Object Detection; 3D Object Detection; image retrieval; metric learning, face recognition; person/vehicle re-identification; instance segmentation; semantic segmentation; ... paper: summary: In 2020, it is to be held virtually due to covid-19 pandemic. LaTeX2e files for the text, PS/EPS or PDF/JPG files for all figures. Contribute to xkumiyu/cvpr2020-papers development by creating an account on GitHub. You will see that the conference name and the names of the volume editors are entered in advance. The paper was accepted to CVPR 2020, the leading conference in computer vision. Hence, do not reduce the paper length e.g. My submission package contains ONE compiled pdf file for the camera-ready version to go on Springerlink. Yi Wang, Ying-Cong Chen, Xiangyu Zhang, Jian Sun, Jiaya Jia. References are to be supplied as BBL files to avoid omission of data while conversion from BIB to BBL. Submissions to ECCV 2020 are confidential: they will become publicly available only if the paper is accepted for publication, and only after the camera ready version is received. One author may sign on behalf of all of the authors of a particular paper, provided that permission to do so has been accorded by the other authors in advance. ECCV 2020 接收文章列表: European Computer Vision Association总计1361篇,Oral 104篇,Spotlight 161篇,Poster 1096篇。 其中曾引起广泛影响的Facebook新式目标检测算法 End-to … The list of accepted papers can be found here. If you use a different document processing system then see the ECCV author instruction page. If you have any queries regarding copyright, please contact Springer or the Publication Chairs ([email protected]) well in advance of publication. I have used the same name spelling in all my papers accepted to ECCV and ECCV Workshops. This is achieved through embedding ORCID identifiers in key workflows, such as research profile maintenance, manuscript submissions, grant applications and patent applications. ECCV Copyright form (Updated 16-06-2020) Please fill this form, sign it, and scan it as a single PDF file, named XXXX-copyright.PDF, where XXXX is the four-digit paper ID (zero-padded if necessary). ECCV 2020: Some Highlights The 2020 European Conference on Computer Vision took place online, from 23 to 28 August, and consisted of 1360 papers, divided into 104 orals, 160 spotlights and the rest of 1096 papers as posters. Any additional authors who wish to attend ECCV’20 should use the delegate registration option. by reducing vertical spaces between paragraphs or the like, as this will be corrected by Springer, possibly resulting in an increase of the paper length. The review process of ECCV 2020 is also confidential. Paper fee is paid by the camera ready deadline. We hope that this conference has stimulated your interest and research in computer vision. This file will be submitted separately. We encourage all contributing authors to also apply for an individual ORCID-ID at www.orcid.org and include these in their paper with \OrcidID command. ECCV 2020 : European Conference on Computer Vision Conference Series : European Conference on Computer Vision Link: https://eccv2020.eu/ When: Aug 23, 2020 - Aug 28, 2020 Where: Glasgow: Submission Deadline: TBD Call For Papers [Empty] Related Resources . Our typesetters require the author-created pdfs in order to check the proper representation of symbols, figures, etc. The European Conference on Computer Vision (ECCV) 2020 is being hosted virtually from August 23rd - 28th. For student registration to cover 1 short paper, please use this link to register as a Non-ACM/SIGMM member and select “Non-Member Author (Student)”. The European Conference on Computer Vision (ECCV) is one of the top computer vision conferences in the world. Let us know if more papers can be added to this table. For your paper to be published, you must pay the Publication Fee before the camera-ready deadline. One paper was accepted to ECCV 2020. CVPR 2020 论文开源项目合集. ORCID (Open Researcher and Contributor ID) hosts a registry of unique researcher identifiers and a transparent method of linking research activities to these identifiers. See the list of topics here. The objective is to assess if the experiments are reproducible, and to determine if the conclusions of the paper are supported by your findings. We need all the source files (LaTeX files, style files, special fonts, figures, bib-files) that are required to compile papers, as well as the camera ready PDF. News [07/2020] ~NEW Two papers have been accepted by ECCV2020! Manually set the scale encoding here, which gives the left panel of Table 2 in the main paper. For each paper, one ZIP-file has to be prepared and submitted via the ECCV 2020 Submission Website, using the password you received with your initial registration on that site. Resolution Switchable Networks for Runtime Efficient Image Classification, WeightNet: Revisiting the Design Space of Weight Networks, Suppressing Mislabeled Data via Grouping and Self-Attention, L2 Norm: A Generic Visualization Approach for Convolutional Neural Networks, FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning, Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches, PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer, Faster AutoAugment: Learning Augmentation Strategies using Backpropagation, ATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering, End-to-End Object Detection with Transformers, BorderDet: Border Feature for Dense Object Detection, Side-Aware Boundary Localization for More Precise Object Detection, PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments, AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling, A General Toolbox for Understanding Errors in Object Detection, MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection, Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training, OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features, New Threats against Object Detector with Non-Local Block, Dense RepPoints: Representing Visual Objects with Dense Point Sets, Large Batch Optimization for Object Detection: Training COCO in 12 Minutes, Dive Deeper Into Box for Object Detection, CenterNet Heatmap Propagation for Real-time Video Object Detection, Probabilistic Anchor Assignment with IoU Prediction for Object Detection, HoughNet: Integrating near and long-range evidence for bottom-up object detection, Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN, RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving, Finding Your (3D) Center: 3D Object Detection Using a Learned Loss, Rotation-robust Intersection over Union for 3D Object Detection, Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots, Automated Data Augmentation Significantly Improves 3D Object Detection, Learning and aggregating deep local descriptors for instance-level recognition, Targeted Attack for Deep Hashing based Retrieval, Online Invariance Selection for Local Feature Descriptors, ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval, S2DNet: Learning accurate correspondences for sparse-to-dense feature matching, Unifying Deep Local and Global Features for Image Search, SOLAR: Second-Order Loss and Attention for Image Retrieval, Metric learning: cross-entropy vs. pairwise losses, pherical Feature Transform for Deep Metric Learning, BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition, Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification, Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network, Identity-Guided Human Semantic Parsing Learning for Person Re-Identification, The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification, Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup, Prediction, Recovery and Identification: Adaptive Low-Resolution Person Re-Identification, Conditional Convolutions for Instance Segmentation, SIP: Spatial Information Preservation for Fast Instance Segmentation, Learning with Noisy Class Labels for Instance Segmentation, The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation, LevelSet R-CNN: A Deep Variational Method for Instance Segmentation, Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation, Semantic Flow for Fast and Accurate Scene Parsing, Object-Contextual Representations for Semantic Segmentation, GINet: Graph Interaction Network for Scene Parsing, Blended Grammar Network for Human Parsing, EfficientFCN: Holistically-guided Decoding for Semantic Segmentation, Learning to Predict Context-adaptive Convolution for Semantic Segmentation, Segment as Points for Efficient Online Multi-Object Tracking and Segmentation, Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking, TAO: A Large-scale Benchmark for Tracking Any Object, Towards Real-time MOT: A Joint Solution for Detection and Appearance Embedding, Learning Feature Embeddings for Discriminant Model based Tracking, Learning Object-aware Anchor-free Networks for Real-time Object Tracking, PG-Net: Pixel to Global Matching Network for Visual Tracking Know Your Surroundings: Exploiting Scene Information for Object Tracking, PiP: Planning-informed Trajectory Prediction for Autonomous Driving, Detecting Human-Object Interactions with Action Co-occurrence Priors, UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection, Visual Compositional Learning for Human Object Interaction Detection, Polysemy Deciphering Network for Human-Object Interaction Detection, Self6D: Self-Supervised Monocular 6D Object Pose Estimation, End-to-End Estimation of Multi-Person 3D Poses from Multiple Cameras, HMOR: Hierarchical Multi-person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation, Learning Delicate Local Representations for Multi-Person Pose Estimation, SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation, Adversarial Semantic Data Augmentation for Human Pose Estimation, Occlusion-Aware Siamese Network for Human Pose Estimation, emporal Keypoint Matching and Refinement Network for Pose Estimation and Tracking, Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification, Knowledge Transfer via Dense Cross-layer Mutual-distillation, Feature Normalized Knowledge Distillation for Image Classification, Learning to Detect Open Classes for Universal Domain Adaptation, On the Effectiveness of Image Rotation for Open Set Domain Adapation, Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation, Domain Adaptation through Task Distillation, https://github.com/Yonghongwei/Gradient-Centralization, https://github.com/bl0/negative-margin.few-shot, https://github.com/open-mmlab/mmdetection, https://github.com/justimyhxu/Dense-RepPoints, https://github.com/rosinality/ocr-pytorch, https://github.com/xingyizhou/CenterTrack, 论文推荐|Mask TextSpotter:An End-to-End Trainable Neural Network, WordPress添加链接-Wordpress教程|TutorialsPoint后端教程合集【Tutorialspoint 后端教程集合】, WordPress编辑链接-Wordpress教程|TutorialsPoint后端教程合集【Tutorialspoint 后端教程集合】, Wordpress删除链接-Wordpress教程|TutorialsPoint后端教程合集【Tutorialspoint 后端教程集合】. 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