https://github.com/vadimkantorov/contextlocnet
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
Papers with Code 아카이브
Weakly-Supervised Object Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 30
Weakly supervised object localization (WSOL) learns to localize objects with only image-level labels, no object level labels (bonding boxes, etc.,) is needed. It is more attractive since image-level labels are much easier and cheaper to obtain.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vasgaowei/ts-cam-voc
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
https://github.com/vlue-c/PyTorch-Explanations
https://github.com/vlue-c/Visual-Explanation-Methods-PyTorch
https://github.com/windstormer/Cfd-CAM
https://github.com/yeezhu/SPN.pytorch
Soft Proposal Networks for Weakly Supervised Object Localization
https://github.com/zdcuob/Fully-Convlutional-Neural-Networks-for-state-of-the-art-time-series-classification-
https://github.com/zhengshou/AutoLoc
https://github.com/zphang/saliency_investigation
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.