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Chenyi Xiong

2 accepted papers

2026

HyLoVQA: Dynamic Hypernetwork-Generated Low-Rank Adaptation for Continual Visual Question Answering

IJCAI 2026

Continual Visual Question Answering (VQA) requires learning from non-stationary streams of visual inputs and questions while preserving past knowledge. Most prior methods adapt by updating a largely shared parameter set. This often leads to cross-level task interference, hindering accurate adaptatio

Cited by 0Scholar
2026

MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering

AAAI 2026technical

Visual Question Answering (VQA) requires models to reason over multimodal information, combining visual and textual data. With the development of continual learning, significant progress has been made in retaining knowledge and adapting to new information in the VQA domain. However, current methods

Cited by 0SourcePDFScholar