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cs.CV2026
Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning
Mingkang Dong, Hongyi Cai, Xiwen Lei +3
Multimodal instruction tuning is the de facto recipe for adapting vision language models (VLMs), yet instruction data are highly redundant, making data selection critical for train…
cs.CV2026
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning
Mingkang Dong, Hongyi Cai, Jie Li +4
The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely require visual reasoning. However, existi…
cs.CV2025
Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
Ziyan Liu, Yeqiu Chen, Hongyi Cai +4
Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and action execution. In real-time deployme…