[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
[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
[7] Open-world Object Detection
[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
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