The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information available, primarily due to their reliance on simplistic and coarse utilization of image attributes. Recent advances in artificial intelligence generated content have made it possible to automatically generate plentiful fine-grained attribute descriptions and make full use of them. Thereby, this paper explores the potential of using the generated multiple person attributes as prompts in ReID tasks with off-the-shelf (large) models for more accurate retrieval results. To this end, we present a new framework called Multi-Prompts ReID (MP-ReID), based on prompt learning and language models, to fully dip fine attributes to assist ReID task. Specifically, MP-ReID first learns to hallucinate diverse, informative, and promptable sentences for describing the query images. This procedure includes (i) explicit prompts of which attributes a person has and furthermore (ii) implicit learnable prompts for adjusting/conditioning the criteria used towards this person identity matching. Explicit prompts are obtained by ensembling generation models, such as ChatGPT and VQA models. Moreover, an alignment module is designed to fuse multi-prompts (i.e., explicit and implicit ones) progressively and mitigate the cross-modal gap. Extensive experiments on the existing attribute-involved ReID datasets, namely, Market1501 and DukeMTMC-reID, demonstrate the effectiveness and rationality of the proposed MP-ReID solution.

翻译：细粒度的属性描述能够显著补充行人图像中有价值的语义信息，这对行人重识别任务的成败至关重要。然而，当前的重识别算法通常未能有效利用丰富的上下文信息，这主要归因于它们对图像属性的使用方式过于简单和粗糙。近年来人工智能生成内容的发展使得自动生成大量细粒度属性描述并充分利用它们成为可能。为此，本文探索了在重识别任务中利用现成（大型）模型将生成的多行人属性作为提示的潜力，以获得更精确的检索结果。基于此，我们提出了一种名为多提示重识别（MP-ReID）的新框架，该框架基于提示学习和语言模型，充分利用细粒度属性来辅助重识别任务。具体而言，MP-ReID首先学习生成用于描述查询图像的多样化、信息丰富且可提示的语句。这一过程包括：(i) 关于行人具有哪些属性的显式提示，以及 (ii) 用于调整/调节该行人身份匹配标准的隐式可学习提示。显式提示通过集成生成模型（如ChatGPT和视觉问答模型）获得。此外，我们设计了一个对齐模块来逐步融合多提示（即显式与隐式提示），并缩小跨模态差异。在现有包含属性的重识别数据集（即Market1501和DukeMTMC-reID）上进行的大量实验，证明了所提出的MP-ReID方案的有效性与合理性。