Large-scale language-image pre-trained models (e.g., CLIP) have shown superior performances on many cross-modal retrieval tasks. However, the problem of transferring the knowledge learned from such models to video-based person re-identification (ReID) has barely been explored. In addition, there is a lack of decent text descriptions in current ReID benchmarks. To address these issues, in this work, we propose a novel one-stage text-free CLIP-based learning framework named TF-CLIP for video-based person ReID. More specifically, we extract the identity-specific sequence feature as the CLIP-Memory to replace the text feature. Meanwhile, we design a Sequence-Specific Prompt (SSP) module to update the CLIP-Memory online. To capture temporal information, we further propose a Temporal Memory Diffusion (TMD) module, which consists of two key components: Temporal Memory Construction (TMC) and Memory Diffusion (MD). Technically, TMC allows the frame-level memories in a sequence to communicate with each other, and to extract temporal information based on the relations within the sequence. MD further diffuses the temporal memories to each token in the original features to obtain more robust sequence features. Extensive experiments demonstrate that our proposed method shows much better results than other state-of-the-art methods on MARS, LS-VID and iLIDS-VID. The code is available at https://github.com/AsuradaYuci/TF-CLIP.

翻译：大规模语言-图像预训练模型（如CLIP）已在多项跨模态检索任务中展现出卓越性能。然而，如何将此类模型学到的知识迁移至视频行人重识别领域这一问题尚未得到充分探索。此外，现有行人重识别基准数据集普遍缺乏高质量文本描述。为应对这些挑战，本文提出一种新颖的单阶段无文本CLIP学习框架TF-CLIP，专门用于视频行人重识别。具体而言，我们提取身份特定的序列特征作为CLIP记忆模块以替代文本特征，同时设计序列特定提示模块实现CLIP记忆的在线更新。为捕获时序信息，我们进一步提出时序记忆扩散模块，该模块包含两个关键组件：时序记忆构建模块与记忆扩散模块。从技术角度而言，时序记忆构建模块允许序列内帧级记忆相互通信，并基于序列内部关系提取时序信息；记忆扩散模块则将时序记忆扩散至原始特征的每个标记中，从而获得更鲁棒的序列特征。大量实验表明，本文方法在MARS、LS-VID和iLIDS-VID数据集上的性能显著优于现有最优方法。相关代码已开源至https://github.com/AsuradaYuci/TF-CLIP。