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本文转载自:计算机视觉Daily
前言
距离ECCV 2020 会议结束有段时间了,但其中的论文大多是目前的SOTA,所以非常值得大家花时间阅读学习!
ECCV 2020 有效投稿数为5025,最终收录1361篇论文,录取率是27%。其中104篇 Oral、161篇 Spotlights,剩下皆为Poster。
计算机视觉Daily 将正式系列整理 ECCV 2020的大盘点工作,本文为第一篇:2D 目标检测方向。主要包含:一般的2D目标检测、旋转目标检测、视频目标检测、弱监督、域自适应等方向。
注意1:并不包含3D 目标检测和显著性目标检测,因为这两个方向的论文也是超级多的,后续计算机视觉Daily会专门系统整理,还请关注后续内容。
在计算机视觉Daily后台回复:ECCV2020目标检测,即可下载这49篇论文的PDF
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注意2:中科院、香港中文大学的工作非常之多,太强了!
目录
2D 目标检测
旋转目标检测
视频目标检测
弱监督目标检测
域自适应目标检测
Few-Shot 目标检测
水下目标检测
目标检测对抗攻击
其他2D目标检测
End-to-End Object Detection with Transformers
DETR:基于Transformers的端到端目标检测
作者单位:巴黎第九大学, Facebook AI
论文:https://arxiv.org/abs/2005.12872
代码:https://github.com/facebookresearch/detr
中文解读:
BorderDet: Border Feature for Dense Object Detection
BorderDet:用于密集目标检测的边界特征
Corner Proposal Network for Anchor-free, Two-stage Object Detection
TIDE: A General Toolbox for Identifying Object Detection Errors
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Side-Aware Boundary Localization for More Precise Object Detection
AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection
Soft Anchor-Point Object Detection
Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
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
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes
Hierarchical Context Embedding for Region-based Object Detection
Dive Deeper Into Box for Object Detection
Probabilistic Anchor Assignment with IoU Prediction for Object Detection
HoughNet: Integrating near and long-range evidence for bottom-up object detection
Learning Data Augmentation Strategies for Object Detection
Quantum-soft QUBO Suppression for Accurate Object Detection
Dense RepPoints: Representing Visual Objects with Dense Point Sets
Representation Sharing for Fast Object Detector Search and Beyond
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
Arbitrary-Oriented Object Detection with Circular Smooth Label
Video Object Detection via Object-level Temporal Aggregation
Learning Where to Focus for Efficient Video Object Detection
Mining Inter-Video Proposal Relations for Video Object Detection
CenterNet Heatmap Propagation for Real-time Video Object Detection
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Enabling Deep Residual Networks for Weakly Supervised Object Detection
UFO²: A Unified Framework towards Omni-supervised Object Detection
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
Improving Object Detection with Selective Self-Supervised Self-Training
Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN
Adaptive Object Detection with Dual Multi-Label Prediction
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector
Adapting Object Detectors with Conditional Domain Normalization
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
Dual Refinement Underwater Object Detection Network
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
New Threats against Object Detector with Non-local Block
GeoGraph: Graph-based multi-view object detection with geometric cues end-to-end
Object Detection with a Unified Label Space from Multiple Datasets
LabelEnc: A New Intermediate Supervision Method for Object Detection
PackDet: Packed Long-Head Object Detector
论文PDF下载
上述49篇论文的PDF已全部打包好,在计算机视觉Daily公众号后台回复:ECCV2020目标检测,即可下载访问
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