Abstract
Given a video with $T$ frames, frame sampling is a task to select $N \ll T$ 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 $\binom{T}{N}$, especially when $N$ gets large. To address this challenge, we introduce a novel perspective of reducing the search space from $O(T^N)$ to $O(T)$. Instead of exploring the entire $O(T^N)$ 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 $T$.
Abstract (translated)
给定一个具有 $T$ 帧的视频，帧采样是将 $N$ 帧的选择任务，以最大化固定视频分类器的性能。不仅仅是暴力搜索，而且大多数现有方法都存在其巨大的搜索空间 $\binom{T}{N}$，尤其是在 $N$ 较大时。为解决这个问题，我们引入了一种新的观点，将搜索空间从 $O(T^N)$ 减少到 $O(T)$。我们提出的半最优策略基于每个帧的独立估计值，选择前 $N$ 帧，大大减少了计算复杂度。我们验证，我们的半最优策略可以有效地近似最优策略，特别是在实际设置下。此外，通过在各种数据集和模型架构上进行广泛的实验，我们证明，学习我们的半最优策略可以确保稳定和高性能，而不会影响 $N$ 和 $T$ 的值。
URL
https://arxiv.org/abs/2409.05260
