VideoSEMA: Efficient Mamba-Like Attention Model for Video Understanding
Researchers introduce VideoSEMA, a split space-time attention model for video classification that uses a scalable Mamba-like attention block for spatial processing and softmax temporal attention. VideoSEMA outperforms heavier vision transformer and Mamba models on the K400 benchmark and achieves leading top-1 accuracy among models of similar size on SSv2. The model also maintains accuracy better than VideoMamba as image resolution increases, without requiring fine-tuning.
Why it matters: VideoSEMA demonstrates a more efficient and scalable approach to video understanding, achieving strong benchmark results with lower computational cost.
Full story at: arXiv Computer Vision ↗