在潜在空间压缩视频数据集，显著提升小样本下的模型性能。
Latent Video Dataset Distillation
- 在潜在空间而非像素空间进行视频数据蒸馏，利用先进编码器提取特征。
- 在HMDB51（IPC 1）和MiniUCF（IPC 5）上分别提升2.6%和7.8%准确率。
- 无需训练即可进一步压缩，适合资源受限场景的视频模型部署。
数据蒸馏在图像数据集高压缩场景中已表现出优异效果。由于视频数据本身具有更高冗余度，现有视频数据蒸馏方法主要集中在像素空间压缩，忽视了现代文生图与文生视频模型广泛采用的潜在空间进展。本文提出一种全新的潜在空间视频数据蒸馏方法，采用最先进的变分编码器，并引入多样性感知的数据选择策略，以选取兼具代表性与多样性的样本。此外，我们设计了一种简单且无需训练的压缩方法，进一步降低蒸馏后潜在数据集规模。结合上述技术，本方法在所有测试数据集上均达到新最优性能：在HMDB51（IPC 1）上提升2.6%，在MiniUCF（IPC 5）上提升7.8%。代码已开源。
原文摘要 · Abstract (English)
Dataset distillation has demonstrated remarkable effectiveness in high-compression scenarios for image datasets. While video datasets inherently contain greater redundancy, existing video dataset distillation methods primarily focus on compression in the pixel space, overlooking advances in the latent space that have been widely adopted in modern text-to-image and text-to-video models. In this work, we bridge this gap by introducing a novel video dataset distillation approach that operates in the latent space using a state-of-the-art variational encoder. Furthermore, we employ a diversity-aware data selection strategy to select both representative and diverse samples. Additionally, we introduce a simple, training-free method to further compress the distilled latent dataset. By combining these techniques, our approach achieves a new state-of-the-art performance in dataset distillation, outperforming prior methods on all datasets, e.g. on HMDB51 IPC 1, we achieve a 2.6% performance increase; on MiniUCF IPC 5, we achieve a 7.8% performance increase. Our code is available at https://github.com/liningresearch/Latent_Video_Dataset_Distillation.
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