eccv2020_paperlist

• Image Recognition
• 2D Object Detection
• 3D Object Detection
• image retrieval
• metric learning, face recognition
• person/vehicle re-identification
• instance segmentation
• semantic segmentation
• tracking
• motion prediction for autonomous driving
• human object interaction detection
• pose estimation
• knowledge distillation
• domain adaptation
• action recognition
• depth estimation

Image Recognition

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Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation

• paper: https://arxiv.org/abs/2003.08866
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MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution

• paper: https://arxiv.org/abs/1909.12978
• summary:
• code: https://github.com/taoyang1122/MutualNet

Hybrid Models for Open Set Recognition

• paper: https://arxiv.org/abs/2003.12506
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Gradient Centralization: A New Optimization Technique for Deep Neural Networks

• paper: https://arxiv.org/abs/2004.01461
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• code: https://github.com/Yonghongwei/Gradient-Centralization

Multi-task Learning Increases Adversarial Robustness

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Rethinking Bottleneck Structure for Efficient Mobile Network Design

• paper: https://arxiv.org/abs/2007.02269
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Negative Margin Matters: Understanding Margin in Few-shot Classification

• paper: https://arxiv.org/abs/2003.12060
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• code: https://github.com/bl0/negative-margin.few-shot

Dynamic Group Convolution for Accelerating Convolutional Neural Networks

• paper: https://arxiv.org/abs/2007.04242
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• code: https://github.com/zhuogege1943/dgc

PROFIT: A Novel Training Method for sub-4-bit MobileNet Models

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Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets

• paper: https://arxiv.org/abs/2007.09654
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• code: https://github.com/wutong16/DistributionBalancedLoss

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URIE: Universal Image Enhancement for Visual Recognition in the Wild

• paper: https://arxiv.org/abs/2007.08979
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DADA: Differentiable Automatic Data Augmentation

• paper: https://arxiv.org/abs/2003.03780
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• code: https://github.com/VDIGPKU/DADA

AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning

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Momentum Batch Normalization for Deep Learning with Small Batch Size

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Hard negatives examples are hard, but useful

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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

• paper: https://arxiv.org/abs/2003.11539
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Topic-aware Multi-Label Classification

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Resolution Switchable Networks for Runtime Efficient Image Classification

• paper: https://arxiv.org/abs/2007.09558
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• code: https://github.com/yikaiw/RS-Nets

Suppressing Mislabeled Data via Grouping and Self-Attention

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Attentive Normalization

• paper: https://arxiv.org/abs/1908.01259
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L2 Norm: A Generic Visualization Approach for Convolutional Neural Networks

• paper: https://arxiv.org/abs/2007.09748
• code: https://github.com/ahmdtaha/constrained_attention_filter
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FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

• paper: https://arxiv.org/abs/2007.08505
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Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches

• paper: https://arxiv.org/abs/2003.03836
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PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

• paper: https://arxiv.org/abs/2007.06191
• code: https://github.com/d-li14/PSConv
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Faster AutoAugment: Learning Augmentation Strategies using Backpropagation

• paper: https://arxiv.org/abs/1911.06987
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GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering

• paper: https://arxiv.org/abs/2002.11863
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Volumetric Transformer Networks

• paper: https://arxiv.org/abs/2007.09433
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OnlineAugment: Online Data Augmentation with Less Domain Knowledge

• paper: https://arxiv.org/abs/2007.09271
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2D Object Detection

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End-to-End Object Detection with Transformers

• paper: https://arxiv.org/abs/2005.12872
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• code: https://github.com/facebookresearch/detr

BorderDet: Border Feature for Dense Object Detection

• paper: https://arxiv.org/abs/2007.11056
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• code: https://github.com/Megvii-BaseDetection/BorderDet

spotlight

Side-Aware Boundary Localization for More Precise Object Detection

• paper: https://arxiv.org/abs/1912.04260
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• code: https://github.com/open-mmlab/mmdetection

PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments

• paper: https://arxiv.org/abs/2007.09584
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AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling

• paper: https://arxiv.org/abs/2007.09336
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TIDE: A General Toolbox for Understanding Errors in Object Detection

• paper: https://arxiv.org/abs/2008.08115
• summary: #5
• code: https://github.com/dbolya/tide

Corner Proposal Network for Anchor-free, Two-stage Object Detection

• paper: https://arxiv.org/abs/2007.13816
• summary: #4
• code: https://github.com/Duankaiwen/CPNDet

poster

Soft Anchor-Point Object Detection

• paper: https://arxiv.org/abs/1911.12448
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MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection

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Disentangled Non-local Neural Networks

• paper: https://arxiv.org/abs/2006.06668
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Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

• paper: https://arxiv.org/abs/2004.06002
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OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features

• paper: https://arxiv.org/abs/2003.06800
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• code: https://github.com/aosokin/os2d

New Threats against Object Detector with Non-Local Block

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Dense RepPoints: Representing Visual Objects with Dense Point Sets

• paper: https://arxiv.org/abs/1912.11473
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• code: https://github.com/justimyhxu/Dense-RepPoints

Large Batch Optimization for Object Detection: Training COCO in 12 Minutes

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Dive Deeper Into Box for Object Detection

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CenterNet Heatmap Propagation for Real-time Video Object Detection

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Probabilistic Anchor Assignment with IoU Prediction for Object Detection

• paper: https://arxiv.org/abs/2007.08103
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• code: https://github.com/kkhoot/PAA

HoughNet: Integrating near and long-range evidence for bottom-up object detection

• paper: https://arxiv.org/abs/2007.02355
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• code: https://github.com/nerminsamet/houghnet

Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN

• paper: https://arxiv.org/abs/2007.01571
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Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation

• paper: https://arxiv.org/abs/2007.02846
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3D Object Detection

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RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

• paper: https://arxiv.org/abs/2001.03343
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poster

Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

• paper: https://arxiv.org/abs/2004.02693
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Rotation-robust Intersection over Union for 3D Object Detection

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Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots

• paper: https://arxiv.org/abs/1912.12791
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Automated Data Augmentation Significantly Improves 3D Object Detection

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Pillar-based Object Detection for Autonomous Driving

• paper: https://arxiv.org/abs/2007.10323
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image retrieval

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Learning and aggregating deep local descriptors for instance-level recognition

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Targeted Attack for Deep Hashing based Retrieval

• paper: https://arxiv.org/abs/2004.07955
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Online Invariance Selection for Local Feature Descriptors

• paper: https://arxiv.org/abs/2007.08988
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ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval

• paper: http://www.weixiushen.com/publication/eccv20_ExchNet.pdf
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S2DNet: Learning accurate correspondences for sparse-to-dense feature matching

• paper: https://arxiv.org/abs/2004.01673
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Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval

• paper: https://arxiv.org/abs/2007.12163
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Unifying Deep Local and Global Features for Image Search

• paper: https://arxiv.org/abs/2001.05027
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SOLAR: Second-Order Loss and Attention for Image Retrieval

• paper: https://arxiv.org/abs/2001.08972
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metric learning, face recognition

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Metric learning: cross-entropy vs. pairwise losses

• paper: https://arxiv.org/abs/2003.08983
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poster

Spherical Feature Transform for Deep Metric Learning

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The Group Loss for Deep Metric Learning

• paper: http://arxiv.org/abs/1912.00385
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BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition

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Explainable Face Recognition

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person/vehicle re-identification

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Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

• paper: https://arxiv.org/abs/2007.10315
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Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

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Identity-Guided Human Semantic Parsing Learning for Person Re-Identification

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Faster Person Re-Identification

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The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification

• paper: https://arxiv.org/abs/2004.06271
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Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup

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Prediction, Recovery and Identification: Adaptive Low-Resolution Person Re-Identification

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Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

• paper: https://arxiv.org/abs/2007.01546
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• code: https://github.com/YunpengZhai/MEB-Net

instance segmentation

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Conditional Convolutions for Instance Segmentation

• paper: https://arxiv.org/abs/2003.05664
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SIP: Spatial Information Preservation for Fast Instance Segmentation

• paper: https://arxiv.org/abs/2007.14772
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• code: https://github.com/JialeCao001/SipMask

Learning with Noisy Class Labels for Instance Segmentation

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Boundary-preserving Mask R-CNN

• paper: https://arxiv.org/abs/2007.08921
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• code: https://github.com/hustvl/BMaskR-CNN

The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation

• paper: https://arxiv.org/abs/2007.11978
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• code: https://github.com/twangnh/SimCal

SOLO: Segmenting Objects by Locations

• paper: https://arxiv.org/abs/1912.04488
• summary: #2

LevelSet R-CNN: A Deep Variational Method for Instance Segmentation

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PatchPerPix for Instance Segmentation

• paper: https://arxiv.org/abs/2001.07626
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semantic segmentation

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Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

• paper: https://arxiv.org/abs/2003.08440
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Semantic Flow for Fast and Accurate Scene Parsing

• paper: https://arxiv.org/abs/2002.10120
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• code: https://github.com/donnyyou/torchcv

spotlight

Object-Contextual Representations for Semantic Segmentation

• paper: https://arxiv.org/abs/1909.11065
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• code: https://github.com/rosinality/ocr-pytorch

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GINet: Graph Interaction Network for Scene Parsing

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Blended Grammar Network for Human Parsing

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EfficientFCN: Holistically-guided Decoding for Semantic Segmentation

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Learning to Predict Context-adaptive Convolution for Semantic Segmentation

• paper: https://arxiv.org/abs/2004.08222
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SegFix: Model-Agnostic Boundary Refinement for Segmentation

• paper: https://arxiv.org/abs/2007.04269
• summary: #3
• code: https://github.com/openseg-group/openseg.pytorch

tracking

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Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

• paper: https://arxiv.org/abs/2007.01550
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• code: https://github.com/detectRecog/PointTrack

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Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

• paper: https://arxiv.org/pdf/2007.14557.pdf
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• code: https://github.com/pjl1995/CTracker

Tracking objects as points

• paper: https://arxiv.org/abs/2004.01177
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• code: https://github.com/xingyizhou/CenterTrack

TAO: A Large-scale Benchmark for Tracking Any Object

• paper: https://arxiv.org/abs/2005.10356
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• project page: http://taodataset.org/

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Towards Real-time MOT: A Joint Solution for Detection and Appearance Embedding

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Learning Feature Embeddings for Discriminant Model based Tracking

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Learning Object-aware Anchor-free Networks for Real-time Object Tracking

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PG-Net: Pixel to Global Matching Network for Visual Tracking Know Your Surroundings: Exploiting Scene Information for Object Tracking

• paper: https://arxiv.org/abs/2003.11014
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motion prediction for autonomous driving

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PiP: Planning-informed Trajectory Prediction for Autonomous Driving

• paper: https://arxiv.org/abs/2003.11476
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human object interaction detection

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Detecting Human-Object Interactions with Action Co-occurrence Priors

• paper: https://arxiv.org/abs/2007.08728
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• code: https://github.com/Dong-JinKim/ActionCooccurrencePriors/

UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

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Visual Compositional Learning for Human Object Interaction Detection

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Polysemy Deciphering Network for Human-Object Interaction Detection

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pose estimation

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Self6D: Self-Supervised Monocular 6D Object Pose Estimation

• paper: https://arxiv.org/abs/2004.06468
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End-to-End Estimation of Multi-Person 3D Poses from Multiple Cameras

• paper: https://arxiv.org/abs/2004.06239
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HMOR: Hierarchical Multi-person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation

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Learning Delicate Local Representations for Multi-Person Pose Estimation

• paper: http://arxiv.org/abs/2003.04030
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Whole-Body Human Pose Estimation in the Wild

• paper: https://arxiv.org/abs/2007.11858
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• code: https://github.com/jin-s13/COCO-WholeBody

SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation

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Adversarial Semantic Data Augmentation for Human Pose Estimation

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Occlusion-Aware Siamese Network for Human Pose Estimation

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emporal Keypoint Matching and Refinement Network for Pose Estimation and Tracking

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knowledge distillation

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Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification

• paper: https://arxiv.org/abs/2001.01536
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Knowledge Transfer via Dense Cross-layer Mutual-distillation

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Matching Guided Distillation

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Feature Normalized Knowledge Distillation for Image Classification

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domain adaptation

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Learning to Detect Open Classes for Universal Domain Adaptation

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On the Effectiveness of Image Rotation for Open Set Domain Adapation

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Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

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Domain Adaptation through Task Distillation

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Action Recognition

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MotionSqueeze: Neural Motion Feature Learning for Video Understanding

• paper: https://arxiv.org/abs/2007.09933
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Depth Estimation

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Feature-metric Loss for Self-supervised Learning of Depth and Egomotion

• paper: https://arxiv.org/abs/2007.10603
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