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New Mamba-like attention model VideoSEMA boosts video understanding performance

By PulseAugur Editorial · [5 sources] ·

Researchers have introduced VideoSEMA, a novel Mamba-like attention model designed for efficient and scalable video understanding. This model utilizes a split space-time attention mechanism, combining local window attention with global averaging in its spatial component and softmax temporal attention in its temporal component. VideoSEMA demonstrates superior performance on benchmark datasets like K400 and SSv2 compared to existing vision transformers and Mamba models, particularly in its graceful degradation of accuracy as image resolution increases. The work also highlights the potential for extending VideoSEMA to handle longer videos through dilated or sparse temporal attention. AI

IMPACT This new Mamba-like architecture offers improved efficiency and performance for video understanding tasks, potentially influencing future developments in video AI.

RANK_REASON The cluster contains research papers detailing a new model architecture for video understanding.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New Mamba-like attention model VideoSEMA boosts video understanding performance

COVERAGE [5]

  1. arXiv cs.AI TIER_1 English(EN) · Nhat Thanh Tran, Fanghui Xue andShuai Zhang, Jiancheng Lyu, Yunling Zheng, Yingyong Qi, Jack Xin ·

    VideoSEMA: a scalable and efficient Mamba-like attention for video understanding

    arXiv:2607.14711v1 Announce Type: cross Abstract: We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time. In each…

  2. arXiv cs.AI TIER_1 English(EN) · Jack Xin ·

    VideoSEMA: a scalable and efficient Mamba-like attention for video understanding

    We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time. In each frame, SEMA attention applies a local window atte…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

    Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse…

  4. arXiv cs.CV TIER_1 English(EN) · Xinhao Li, Yuhan Zhu, Xiangyu Zeng, Yuhao Dong, Haoning Wu, Zhiqiu Zhang, Yuandong Yang, Changlian Ma, Qingyu Zhang, Yansong Shi, Xinyu Chen, Haoran Chen, Zizheng Huang, Jun Zhang, Kun Ouyang, Lin Sui, Ziang Yan, Yicheng Xu, Chenting Wang, Yinan He, Hong… ·

    VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

    arXiv:2607.14935v1 Announce Type: new Abstract: Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. …

  5. arXiv cs.CV TIER_1 English(EN) · Limin Wang ·

    VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

    Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse…

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