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Haoyan Xu

2 accepted papers

2026

CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning

AAAI 2026technical

Modern large vision-language models (LVLMs) convert each input image into a large set of tokens that far outnumber the text tokens. Although this improves visual perception, it also introduces severe image token redundancy. Because image tokens contain sparse information, many contribute little to r

Cited by 0SourcePDFScholar
2026

SepPrune: Structured Pruning for Efficient Deep Speech Separation

AAAI 2026technical

Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low-latency speech processing in real-time applications. In this paper, we propose

Cited by 0SourcePDFScholar