SCMM: Calibrating Cross-modal Representations for Text-Based Person Search
It addresses the challenge of retrieving person images from text descriptions for applications like surveillance, with incremental improvements over existing methods.
The paper tackles the problem of text-based person search by bridging the semantic gap between images and text descriptions, achieving state-of-the-art performance with Rank1 accuracies of 73.81%, 64.25%, and 57.35% on three benchmarks.
Text-Based Person Search (TBPS) aims to retrieve target person images from a large-scale gallery using natural language descriptions, posing fundamental challenges in cross-modal representation learning. Existing methods often struggle to bridge the semantic gap between heterogeneous modalities while capturing fine-grained correspondences essential for discriminating visually similar individuals. To address these challenges, we propose Sew Calibration and Masked Modeling (SCMM), a unified framework that calibrates cross-modal representations through complementary learning mechanisms. Notably, SCMM introduces two novel components: a sew calibration loss that dynamically aligns image-text features using quality-guided adaptive margins based on textual information density, and a masked caption modeling loss that establishes fine-grained cross-modal correspondences through transformer-based masked prediction. Additionally, the sew calibration mechanism implements bidirectional constraints to effectively compress same-identity features in the shared embedding space, while the masked modeling component leverages a cross-modal decoder to learn word-level visual-textual relationships, enabling discrimination of subtle attribute differences. Our dual-encoder architecture achieves an effective balance between representation capability and computational efficiency by employing a training-only decoder design. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReID benchmarks demonstrate that SCMM achieves state-of-the-art performance with Rank1 accuracies of 73.81%, 64.25%, and 57.35%, respectively. Comprehensive ablation studies validate the effectiveness of each proposed component.