Papers with Code 아카이브
2D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wangck20/opera
공식 코드OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions
https://github.com/wangck20/v2m
공식 코드V2M: Visual 2-Dimensional Mamba for Image Representation Learning
https://github.com/wanggrun/Adaptively-Connected-Neural-Networks
공식 코드https://github.com/wangguanan/denoiserep
공식 코드https://github.com/wangguojun2018/CenterNet3d
공식 코드https://github.com/wangguojun2018/MVAF-Net
공식 코드https://github.com/wanghao9610/ov-dino
공식 코드OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
https://github.com/wangle53/anodfdnet
공식 코드AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
https://github.com/wangsch10/v2x-dgpe
공식 코드V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection
https://github.com/wangsr126/RDSNet
공식 코드RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
https://github.com/wangsr126/ndet
공식 코드Narrowing the Gap: Improved Detector Training with Noisy Location Annotations
https://github.com/wangwen-whu/wtw-dataset
공식 코드https://github.com/wangxiyang2022/YONTD-MOT
공식 코드You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
https://github.com/wangyongqi558/METOR
공식 코드https://github.com/wangys16/ohda
공식 코드Syn-to-Real Unsupervised Domain Adaptation for Indoor 3D Object Detection
https://github.com/wangyueft/detr3d
공식 코드DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
https://github.com/wangzcbruce/dhd
공식 코드https://github.com/wanzysky/SlenderObjDet
공식 코드https://github.com/warlockwendell/aggdet
공식 코드Training-free Boost for Open-Vocabulary Object Detection with Confidence Aggregation
https://github.com/warriordby/lfx
공식 코드https://github.com/waterdisappear/SARATR-X
공식 코드SARATR-X: Toward Building A Foundation Model for SAR Target Recognition
https://github.com/waterscenes/waterscenes
공식 코드https://github.com/waveboo/clis
공식 코드https://github.com/waymo-research/waymo-open-dataset
공식 코드LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection
https://github.com/wb014/FreeGS
공식 코드Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding
https://github.com/wchao0601/m4-sar
공식 코드https://github.com/wcyjerry/UCFnet
공식 코드Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again
https://github.com/wcyjerry/basicisos
공식 코드Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again
https://github.com/wdhudiekou/irfs
공식 코드https://github.com/wdhudiekou/stsc
공식 코드Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
https://github.com/wds320/AAAI_Event_based_detection
공식 코드Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
https://github.com/we1pingyu/CALD
공식 코드https://github.com/we1pingyu/DSHNet
공식 코드Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection
https://github.com/weathon/Lang-Gas
공식 코드https://github.com/weathon/vcos
공식 코드ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.