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
This work proposes DeltaWAM, which jointly predicts visual deltas and actions using dense-anchor, sparse-delta, and action streams, with three architectures that differ in representation and computation sharing, and develops Streaming Delta Memory (SDM), which updates cached anchor context with compact observed deltas,...
Han Yan, Zi-Shang Xiang, Hao-Kai Jiang et al.· 0 citations
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