arXiv AI
VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs
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VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs
Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates context windows and incurs prohibitive costs. Curre...
arXiv Computation and Language
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ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding
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Diagnosing and Correcting Bias in MLLM for Long Video Understanding
The paper introduces S‑MME, a benchmark for diagnosing shortcut‑biased behavior in Multimodal Large Language Models (MLLMs) on long‑video question answering. It proposes CLEAR, a training‑free framework that estimates and mitigates bias by treating the full video‑question pair as a complete view and intervening on hidden states during inference. Experiments on five benchmarks demonstrate that CLEAR improves prediction accuracy and robustness across different MLLMs.
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Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding
arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.
arXiv Machine Learning
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FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding
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arXiv Machine Learning
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GleanVID: Complementary Token Selection for Efficient Video Large Language Models
arXiv:2609.37042v1 Announce Type: cross Abstract: Video Large Language Models (VideoLLMs) have achieved strong video understanding capabilities but incur substantial inference overhead due to the lar...