Composite-Attribute Person Re-Identification via Pose-Guided Disentanglement

Recent advancements in vision-language models have enabled multi-modal person re-identification (Re-ID), where the system takes both an image and a text query to identify matching individuals. While previous state-of-the-art methods perform well with detailed, sentence-level descriptions, we found that their Recall@1 drops by half when using short, keyword-based queries due to ambiguity, training biases, and under-represented attributes. Despite this challenge, short queries provide a more natural and efficient user experience, requiring less effort and allowing for iterative refinement. To address this limitation, we introduce a new problem setting, Composite-Attributes Person Re-ID (CA-ReID), along with a fine-grained composite attribute dataset with queries belonging to varying levels of ambiguity. We further propose two methods: Dense Disentangling Loss to promote attribute-specific embeddings, and Part-Aware Representations that use pose estimation to align textual attributes with relevant body regions. Our method sets a new state of the art on the new CA-ReID benchmark (up to +17% Recall@1) and performs on par with prior methods on existing CC-ReID benchmarks.