arXiv: cs.AI preprintsInternational5 November 2026
Spatio-Temporal Multi-Order Interaction for Video Recognition
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arXiv:2611.05633v1 Announce Type: cross Abstract: Many temporal approaches for video recognition rely on explicit cross-channel aggregation projections, which have quadratic complexity in the number of channels, introducing significant computational overhead. In addition, the spatio-temporal representation used for video recognition is biased either toward local order interactions (for CNNs) or toward global order interactions (for transformers). Starting from the multi-order spatial representation of MogaNet, our method introduces two complementary contributions to tackle these problems. Firs
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