CVPR2021目标检测方向论文

[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/abs/2011.09094

[4] Instance Localization for Self-supervised Detection Pretraining

paper: https://arxiv/abs/2102.08318

code: https://github/limbo0000/InstanceLoc

[5] Dogfight: Detecting Drones from Drone Videos

[6] Multiple Instance Active Learning for Object Detection

[7] Open-world Object Detection

[8] Depth from Camera Motion and Object Detection

paper: https://arxiv/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/abs/2103.01353

code: MM-DistillNet

[10] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

paper: https://arxiv/abs/2103.01903

[11] Categorical Depth Distribution Network for Monocular 3D Object Detection

paper: https://arxiv/abs/2103.01100

[12] Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

paper: https://arxiv/abs/2011.09670

code: https://github/yangxue0827/RotationDetection

[13] Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

paper: https://arxiv/abs/2011.12885

code: https://github/implus/GFocalV2

[14] Unveiling the Potential of Structure-Preserving for Weakly Supervised Object Localization

paper: https://arxiv/abs/2103.04523

[15] MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

paper: https://arxiv/abs/2103.04224

[16] OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

paper: https://arxiv/abs/2103.04507

code: https://github/VDIGPKU/OPANAS

[17] General Instance Distillation for Object Detection

paper: https://arxiv/abs/2103.02340

[18] ST3D: Self-training for Unsupervised Domain Adaptation on 3D ObjectDetection

paper: https://arxiv/abs/2103.05346

code: https://github/CVMI-Lab/ST3D

[19] Center-based 3D Object Detection and Tracking

paper: https://arxiv/abs/2006.11275

code: https://github/tianweiy/CenterPoint

[20] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

paper: https://arxiv/abs/2012.04355

code: https://github/THU17cyz/3DIoUMatch

[21] Simultaneously Localize, Segment and Rank the Camouflaged Objects

paper: https://arxiv/abs/2103.04011

code: https://github/JingZhang617/COD-Rank-Localize-and-Segment

[22] Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

paper: https://arxiv/abs/2011.09670

code: https://github/Thinklab-SJTU/DCL_RetinaNet_Tensorflow

[23] You Only Look One-level Feature

paper: https://arxiv/abs/2103.09460

code: https://github/megvii-model/YOLOF

[24] AQD: Towards Accurate Quantized Object Detection

paper: http://arxiv/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/abs/2103.11402