https://github.com/164140757/scm
공식 코드Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Papers with Code 아카이브
Weakly-Supervised Object Localization
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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/AlexisGuichemerreCode/survey_hist_wsol_sfda
공식 코드Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
https://github.com/AmirooR/Pairwise-Similarity-knowledge-Transfer-WSOL
공식 코드Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
https://github.com/CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
공식 코드Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
https://github.com/Panxjia/SPA_CVPR2021
공식 코드Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
https://github.com/Sierkinhane/ORNet
공식 코드https://github.com/Vision-sejin/FDCNet
공식 코드https://github.com/WinFrand/MELM
공식 코드Min-Entropy Latent Model for Weakly Supervised Object Detection
https://github.com/Winfrand/C-MIL
공식 코드C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
https://github.com/Yangseung/NL-CCAM
공식 코드Combinational Class Activation Maps for Weakly Supervised Object Localization
https://github.com/ZhenyueQin/Research-Softmax-with-Mutual-Information
공식 코드Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
https://github.com/ahmdtaha/l2_caf_pytorch
공식 코드A Generic Visualization Approach for Convolutional Neural Networks
https://github.com/akhilpm/ConvSTN
공식 코드https://github.com/alexisguichemerrecode/pixelcam
공식 코드PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
https://github.com/antonotnawang/hint
공식 코드https://github.com/callsys/genpromp
공식 코드Generative Prompt Model for Weakly Supervised Object Localization
https://github.com/clovaai/wsolevaluation
공식 코드Evaluating Weakly Supervised Object Localization Methods Right
https://github.com/cvi-szu/ccam
공식 코드https://github.com/deepplants/vit-pcm
공식 코드https://github.com/huodongjian0603/crt
공식 코드Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
https://github.com/jacobswan1/MTG-pytorch
공식 코드https://github.com/jazzcharles/cream
공식 코드CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
https://github.com/jbhuang0604/WSL
공식 코드Progressive Representation Adaptation for Weakly Supervised Object Localization
https://github.com/jeromerony/survey_wsl_histology
공식 코드https://github.com/luiszeni/Boosted-OICR
공식 코드Distilling Knowledge from Refinement in Multiple Instance Detection Networks
https://github.com/lwzeng/GC-Net
공식 코드https://github.com/minhlong94/random-mixup
공식 코드Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
https://github.com/naver-ai/calm
공식 코드https://github.com/nexer8/se3d
공식 코드SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
https://github.com/nupurkmr9/Attributional-Robustness
공식 코드Attributional Robustness Training using Input-Gradient Spatial Alignment
https://github.com/nyukat/GMIC
공식 코드https://github.com/raymondyeh07/tv_layers_for_cv
공식 코드https://github.com/ryylcc/owsol
공식 코드https://github.com/saurav-31/vitol
공식 코드ViTOL: Vision Transformer for Weakly Supervised Object Localization
https://github.com/sbelharbi/fcam-wsol
공식 코드F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
https://github.com/sbelharbi/wsol-min-max-entropy-interpretability
공식 코드Min-max Entropy for Weakly Supervised Pointwise Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.