Boosting Generalization Performance in Person Re-identification

Generalizable person re-identification (ReID) has gained significant attention in recent years as it poses greater challenges in recognizing individuals across different domains and unseen scenarios. Existing methods are typically limited to a single visual modality, making it challenging to capture rich semantic information across different domains. Recently, pre-trained vision-language models like CLIP have shown promising performances in various tasks by linking visual representations with their corresponding text descriptions. This enables them to capture diverse high-level semantics from the accompanying text and obtain transferable features. However, the adoption of CLIP has been hindered in person ReID due to the labels being typically index-based rather than descriptive texts. To address this limitation, we propose a novel Cross-modal framework wIth Conditional Prompt (CICP) framework based on CLIP involving the Description Prompt Module (DPM) that pre-trains a set of prompts to tackle the lack of textual information in person ReID. In addition, we further propose the Prompt Generalization Module (PGM) incorporates a lightweight network that generates a conditional token for each image. This module shifts the focus from being limited to a class set to being specific to each input instance, thereby enhancing domain generalization capability for the entire task. Through extensive experiments, we show that our proposed method outperforms state-of-the-art (SOTA) approaches on popular benchmark datasets.