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While most modern video understanding models operate on short-range clips,real-world videos are often several minutes long with semantically consistentsegments of variable length. A common approach to process long videos isapplying a short-form video model over uniformly sampled clips of fixedtemporal length and aggregating the outputs. This approach neglects theunderlying nature of long videos since fixed-length clips are often redundantor uninformative. In this paper, we aim to provide a generic and adaptivesampling approach for long-form videos in lieu of the de facto uniformsampling. Viewing videos as semantically consistent segments, we formulate atask-agnostic, unsupervised, and scalable approach based on Kernel TemporalSegmentation (KTS) for sampling and tokenizing long videos. We evaluate ourmethod on long-form video understanding tasks such as video classification andtemporal action localization, showing consistent gains over existing approachesand achieving state-of-the-art performance on long-form video modeling.

Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding