Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention maps or output features to estimate token importance or redundancy. Several recent approaches also exploit representation changes, but when and how these changes reflec...
Wei-Xuan Li, Zi-Kun Zhou, Xin-Yi Zhuang et al.· 0 citations
We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and 8B scales. Through careful design of our environments, data, and training recipes, MintAct models match the performance of per-domain speci...
Ming-Fei Gao, Rui Tian, Hai-Ming Gang et al.· 0 citations
This paper introduces TinyMem, a model built upon compact multimodal memory for long-form video action detection that outperforms a range of state-of-the-art models on AVA v2.2 while using 5 times fewer memory tokens than the baseline with dense visual memory embeddings.
Rui Tian, Qi Dai, Hang-Rui Hu et al.· IEEE Transactions on Pattern...· 0 citations
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