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InfoResearchPeer-reviewed
Few-Shot Action Recognition via Intra- and Inter-Video Information Maximization
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Summary
The paper proposes Video Information Maximization (VIM), a framework for few-shot video action recognition. VIM uses an adaptive spatial-temporal video sampler to select important frames and amplify critical spatial regions, and a spatial-temporal action alignment model to measure inter-video similarity more precisely. A mutual-information-based training objective jointly maximizes intra- and inter-video information, and experiments on public datasets are reported to demonstrate its effectiveness.