GLD: Global-Local Dynamic Frame Selection for Action Recognition

Video data exhibits significant redundancy. Existing frame sampling techniques, relying solely on global or local strategies, suffer from stability issue and struggle to balance efficiency with accuracy. To address these limitations, we propose a Global-local dynamic (GLD) frame selection method, which adaptively preserves continuous salient action clips while capturing distributed activities through cross-clip sampling. Specifically, we innovatively construct a global-local subsequence (GLS) by splicing the most salient clip and a fixed number of discrete frames randomly sampled from other segments. Then, the GLS is further condensed into the optimal subsequences using beam-pruned dynamic programming. Finally, a lightweight mapping function, comprising a Transformer layer and an MLP, is trained to align the full video features with those of the selected frames for inference. Our method dynamically balances global and local information, improving action recognition accuracy with lower inference costs. Extensive experiments on four datasets demonstrate that GLD outperforms existing sampling strategies.