标题： 动态感知视频蒸馏：基于视频语义优化时间分辨率 显示英文标题

标题： Dynamic-Aware Video Distillation: Optimizing Temporal Resolution Based on Video Semantics

摘要： 随着视觉任务的快速发展以及数据集和模型的扩展，视觉数据集中的冗余减少已成为一个关键的研究领域。为了解决这个问题，数据集蒸馏（DD）作为一种有前途的方法应运而生，它能够在显著减少冗余的同时生成高度紧凑的合成数据集，并保留重要的信息。然而，尽管DD在图像数据集上得到了广泛研究，视频数据集上的DD仍然未被充分探索。由于存在时间信息以及不同类别间不同的冗余程度，视频数据集带来了独特的挑战。现有的DD方法假设所有不同视频语义之间的时间冗余水平相同，这限制了它们在视频数据集上的有效性。在这项工作中，我们提出了动态感知视频蒸馏（DAViD），这是一种基于强化学习（RL）的方法，用于预测合成视频的最佳时间分辨率。提出了一种教师在循环奖励函数来更新RL代理策略。据我们所知，这是首次在视频数据集蒸馏中引入基于视频语义的自适应时间分辨率的研究。我们的方法显著优于现有的DD方法，在性能上有实质性的改进。这项工作为更高效、语义自适应的视频数据集蒸馏研究开辟了道路。 显示英文摘要

摘要： With the rapid development of vision tasks and the scaling on datasets and models, redundancy reduction in vision datasets has become a key area of research. To address this issue, dataset distillation (DD) has emerged as a promising approach to generating highly compact synthetic datasets with significantly less redundancy while preserving essential information. However, while DD has been extensively studied for image datasets, DD on video datasets remains underexplored. Video datasets present unique challenges due to the presence of temporal information and varying levels of redundancy across different classes. Existing DD approaches assume a uniform level of temporal redundancy across all different video semantics, which limits their effectiveness on video datasets. In this work, we propose Dynamic-Aware Video Distillation (DAViD), a Reinforcement Learning (RL) approach to predict the optimal Temporal Resolution of the synthetic videos. A teacher-in-the-loop reward function is proposed to update the RL agent policy. To the best of our knowledge, this is the first study to introduce adaptive temporal resolution based on video semantics in video dataset distillation. Our approach significantly outperforms existing DD methods, demonstrating substantial improvements in performance. This work paves the way for future research on more efficient and semantic-adaptive video dataset distillation research.