https://github.com/mcg-nju/stageinteractor
공식 코드StageInteractor: Query-based Object Detector with Cross-stage Interaction
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mcg-nju/structured-sparse-rcnn
공식 코드https://github.com/mcg-nju/tia
공식 코드Task-specific Inconsistency Alignment for Domain Adaptive Object Detection
https://github.com/mcv-m6-video/mcv-m6-2021-team4
공식 코드https://github.com/mczhuge/ICON
공식 코드https://github.com/mecarill/2pcnet
공식 코드https://github.com/mediabrain-sjtu/where2comm
공식 코드Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
https://github.com/megvii-basedetection/bevdepth
공식 코드BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
https://github.com/megvii-basedetection/bevstereo
공식 코드BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
https://github.com/megvii-basedetection/denseteacher
공식 코드Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
https://github.com/megvii-model/CrowdDetection
공식 코드Detection in Crowded Scenes: One Proposal, Multiple Predictions
https://github.com/megvii-model/DetNAS
공식 코드https://github.com/megvii-model/FunnelAct
공식 코드https://github.com/megvii-model/HINet
공식 코드HINet: Half Instance Normalization Network for Image Restoration
https://github.com/megvii-model/LabelEnc
공식 코드LabelEnc: A New Intermediate Supervision Method for Object Detection
https://github.com/megvii-model/MOTR
공식 코드https://github.com/megvii-model/YOLOF
공식 코드https://github.com/megvii-model/anchordetr
공식 코드Anchor DETR: Query Design for Transformer-Based Object Detection
https://github.com/megvii-model/iter-e2edet
공식 코드https://github.com/megvii-research/AnchorDETR
공식 코드Anchor DETR: Query Design for Transformer-Based Object Detection
https://github.com/megvii-research/ICD
공식 코드Instance-Conditional Knowledge Distillation for Object Detection
https://github.com/megvii-research/LGD
공식 코드https://github.com/megvii-research/NAFNet
공식 코드https://github.com/megvii-research/SOLQ
공식 코드https://github.com/megvii-research/TLC
공식 코드Improving Image Restoration by Revisiting Global Information Aggregation
https://github.com/megvii-research/co-mining
공식 코드Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
https://github.com/megvii-research/cvpr2023-unidistill
공식 코드https://github.com/megvii-research/d2c-sr
공식 코드D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
https://github.com/megvii-research/dcls-sr
공식 코드Deep Constrained Least Squares for Blind Image Super-Resolution
https://github.com/megvii-research/far3d
공식 코드Far3D: Expanding the Horizon for Surround-view 3D Object Detection
https://github.com/megvii-research/mdistiller
공식 코드https://github.com/megvii-research/motr
공식 코드https://github.com/megvii-research/petr
공식 코드PETR: Position Embedding Transformation for Multi-View 3D Object Detection
https://github.com/megvii-research/revcol
공식 코드https://github.com/megvii-research/us3l-cvpr2023
공식 코드Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
https://github.com/meharkhurana03/cm3d
공식 코드Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.