CVPR2021目标检测方向论文
完整的paper list已经出来了。
http://cvpr2021.thecvf.com/node/142
CVPR2021录取结果刚出来,不过离全部的paper list还有一段时间,接下来应该会有人提前将论文贴出来,准备陆续收集相关的论文研究起来。
[1] Distilling Object Detectors via Decoupled Features
[2] Positive-Unlabeled Data Purification in the Wild for Object Detection
[3] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper: https://arxiv.org/abs/2011.09094
code: https://github.com/dddzg/up-detr
[4] Instance Localization for Self-supervised Detection Pretraining
paper: https://arxiv.org/abs/2102.08318
code: https://github.com/limbo0000/InstanceLoc
[5] Dogfight: Detecting Drones from Drone Videos
[6] Multiple Instance Active Learning for Object Detection
paper: https://github.com/yuantn/MIAL/raw/master/paper.pdf
code: https://github.com/yuantn/MIAL
[7] Towards Open World Object Detection
paper: https://arxiv.org/abs/2103.02603
code: https://github.com/JosephKJ/OWOD
[8] Depth from Camera Motion and Object Detection
paper: https://arxiv.org/abs/2103.01468
[9] There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge
paper: https://arxiv.org/abs/2103.01353
code: http://rl.uni-freiburg.de/research/multimodal-distill
[10] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
paper: https://arxiv.org/abs/2103.01903
[11] Categorical Depth Distribution Network for Monocular 3D Object Detection
paper: https://arxiv.org/abs/2103.01100
[12] Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
paper: https://arxiv.org/abs/2011.09670
code: https://github.com/yangxue0827/RotationDetection
[13] Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
paper: https://arxiv.org/abs/2011.12885
code: https://github.com/implus/GFocalV2
[14] Unveiling the Potential of Structure-Preserving for Weakly Supervised Object Localization
paper: https://arxiv.org/abs/2103.04523
[15] MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
paper: https://arxiv.org/abs/2103.04224
[16] OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
paper: https://arxiv.org/abs/2103.04507
code: https://github.com/VDIGPKU/OPANAS
[17] General Instance Distillation for Object Detection
paper: https://arxiv.org/abs/2103.02340
[18] ST3D: Self-training for Unsupervised Domain Adaptation on 3D ObjectDetection
paper: https://arxiv.org/abs/2103.05346
code: https://github.com/CVMI-Lab/ST3D
[19] Center-based 3D Object Detection and Tracking
paper: https://arxiv.org/abs/2006.11275
code: https://github.com/tianweiy/CenterPoint
[20] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
paper: https://arxiv.org/abs/2012.04355
code: https://github.com/THU17cyz/3DIoUMatch
[21] Simultaneously Localize, Segment and Rank the Camouflaged Objects
paper: https://arxiv.org/abs/2103.04011
code: https://github.com/JingZhang617/COD-Rank-Localize-and-Segment
[22] Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
paper: https://arxiv.org/abs/2011.09670
code: https://github.com/Thinklab-SJTU/DCL_RetinaNet_Tensorflow
[23] You Only Look One-level Feature
paper: https://arxiv.org/abs/2103.09460
code: https://github.com/megvii-model/YOLOF
[24] AQD: Towards Accurate Quantized Object Detection
paper: http://arxiv.org/abs/2007.06919
[25] Points as Queries: Weakly Semi-supervised Object Detection by Points
[26] Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
paper: https://arxiv.org/abs/2103.11402
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