Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preserving previously acquired knowledge. Beyond the catastrophic forgetting inherited from conventional graph continual learning, GFSCIL presents two distinctive challenges:...
Jun-Lin Chen, Yu-Han Wang, Xue-Fei Wang et al.· 0 citations
Hierarchical Response Preservation (HiRP) is proposed, a hierarchical response that keeps each historical-class probability and sums new-class probabilities, preserving historical distinctions and aggregate competition while allowing distinctions within the new class group to adapt.
Haopeng Zhang, Yu-Han Wang, Yu-Bing Su et al.· 0 citations
This work introduces a unified formulation for vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visual signals, are all represented as RGB images, while general visual tasks can be converted into a common RGB-to-RGB image editing problem. In...
Ti-Ming Yang, Jinrui Yang, Xinlong Li et al.· 0 citations
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