Abstract
Text-to-image person re-identification (TIReID) aims to retrieve person images from a large gallery given free-form textual descriptions. TIReID is challenging due to the substantial modality gap between visual appearances and textual expressions, as well as the need to model fine-grained correspondences that distinguish individuals with similar attributes such as clothing color, texture, or outfit style. To address these issues, we propose DiCo (Disentangled Concept Representation), a novel framework that achieves hierarchical and disentangled cross-modal alignment. DiCo introduces a shared slot-based representation, where each slot acts as a part-level anchor across modalities and is further decomposed into multiple concept blocks. This design enables the disentanglement of complementary attributes (\textit{e.g.}, color, texture, shape) while maintaining consistent part-level correspondence between image and text. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate that our framework achieves competitive performance with state-of-the-art methods, while also enhancing interpretability through explicit slot- and block-level representations for more fine-grained retrieval results.
Abstract (translated)
文本到图像的人再识别（TIReID）的目标是从大型图片库中检索出给定自由格式的文字描述所对应的人体图像。由于视觉外观与文字表达之间存在显著的模态差距，以及需要建模精细粒度的对应关系以区分具有类似属性（如服装颜色、纹理或着装风格）的个体，TIReID变得极具挑战性。为了应对这些问题，我们提出了一种新的框架DiCo（分离概念表示），该框架实现了层次化和独立的跨模式对齐。 DiCo引入了基于共享槽位的表示方法，在这种方法中，每个槽作为一个跨模态的部分级锚点，并进一步分解成多个概念块。这种设计使得可以将互补属性（如颜色、纹理、形状）分离出来，同时在图像与文本之间保持一致的部分级对应关系。广泛的实验表明，我们的框架在CUHK-PEDES、ICFG-PEDES和RSTPReid数据集上实现了具有竞争力的性能，并通过显式的槽位及块级别表示提升了模型的可解释性，从而能够提供更精细粒度的检索结果。
URL
https://arxiv.org/abs/2601.10053
