Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising from co-occurring and incomplete labels. We argue that the core obstacle is the model's ambiguous boundary between known and unknown knowledge, which undermines historic...
Ao-Ting Zhang, Dong-Bao Yang, Chang Liu et al.· 0 citations
GS-IQA is proposed, a framework that reformulates IQA as a progressive Where--What--How diagnosis, emulating the human perceptual process from an initial glance to closer scrutiny, and consistently surpasses state-of-the-art methods in distortion localization, recognition, and severity estimation.
Ao-Ting Zhang, Ming-Ze Gao, Dong-Bao Yang et al.· 0 citations
This work introduces Emotion Statement Judgement (ESJ), a statement-verification formulation that preserves the expressiveness of the input space while constraining outputs to discriminative judgements, and builds EmObserver, an emotion-oriented MLLM optimized on ESJ through an elaborate multi-stage recipe.
Daiqing Wu, Dong-Bao Yang, Jiashu Yao et al.· arXiv.org· 1 citation
To minimize knowledge interference during fusion, this work presents a gradient-based orthogonal refreshing strategy that projects gradient updates of new domains onto the orthogonal complement of the fused historical subspace, supporting continual adaptation without forgetting.
Aoting Zhang, Dongbao Yang, Chang Liu et al.· arXiv.org· 0 citations
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