Cross-Modal Dependable Subjective Learning for Sketch Person Re-identification

Sketch-based person re-identification Sketch Re-ID enables suspect retrieval when camera images are unavailable by leveraging sketches drawn from human memory. However, the subjectivity in sketches often introduces significant style variation, making it difficult to extract reliable cross-modal features.To address this challenge, we propose a novel Cross-Modal Dependable Subjective Learning CMDSL framework. It consists of a Flexible Feature Aggregation Module FFAM that removes style noise via instance normalization and captures dependable subjective semantics through attention-enhanced residual learning, and a Recognisable Target Centroid Loss RTCL that strengthens discriminability and alignment across modalities.Experiments on MARKET-SKETCH-1K and PKU-Sketch datasets demonstrate that our approach effectively captures consistent subjective cues and achieves state-of-the-art performance under diverse sketch styles.