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Scalable Frame Sampling for Video Classification: A Semi-Optimal Policy Approach with Reduced Search Space
Junho Lee, Jeongwoo Shin, Seung Woo Ko, Seongsu Ha, Joonseok Lee
Given a video with T frames, frame sampling is a task to select N ≪T frames, so as to maximize the performance of a fixed video classifier. Not just brute-force search, but most existing methods suffer from its vast search space of TN, especially when N gets large. To address this challenge, we introduce a novel perspective of reducing the search space from O(Tᴺ) to O(T). Instead of exploring the entire O(Tᴺ) space, our proposed semi-optimal policy selects the top N frames based on the independently estimated value of each frame using per-frame confidence, significantly reducing the computational complexity. We verify that our semi-optimal policy can efficiently approximate the optimal policy, particularly under practical settings. Additionally, through extensive experiments on various datasets and model architectures, we demonstrate that learning our semi-optimal policy ensures stable and high performance regardless of the size of N and T.
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