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Found 7 papers, 3 papers with code
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision.
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment
To address this issue, we study a more practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class distributions of both the unseen source domain and unlabeled target domain are unknown and could be arbitrarily skewed.
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge.
Improved Knowledge Distillation via Adversarial Collaboration
To achieve this goal, a small student model is trained to exploit the knowledge of a large well-trained teacher model.
Semi-Online Knowledge Distillation
However, to the best of our knowledge, KD and DML have never been jointly explored in a unified framework to solve the knowledge distillation problem.
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
(2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes.
Ranked #12 on
Domain Adaptation
on VisDA2017
Retinal Image Segmentation with a Structure-Texture Demixing Network
Retinal image segmentation plays an important role in automatic disease diagnosis.
