Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual tokens results in high computational costs. While many methods have been proposed to reduce the number of visual tokens, most of them rely on heuristics and are prone to...
Ting-Hao Wang, Yi-Chen Guo, Qi-Zhe Zhang et al.· 0 citations
These findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion, and propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework.
Yi-Chen Guo, Ting-Hao Wang, Qi-Zhe Zhang et al.· 1 citation
Improved text-visual attention patterns are introduced to enhance the fidelity of query-aware vision token selection and the Attention Gravity effect is correct, and a rank-based strategy to adaptively determine the sparsification ratio for each layer is introduced.
Yuan Zhang, Junpeng Ma, Qizhe Zhang et al.· IEEE Transactions on Pattern...· 2 citations
Traj-LeWM is proposed, which retains LeWM's local-dynamics objective and endpoint score while introducing a goal-conditioned latent trajectory cost (LTC) that aggregates trajectory-level information as a complementary signal.
Xiaodi Huang, Ziyi Ding, Jingtian Wan et al.· 3 citations
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