GLCE: global-local channel enhancer for fine-grained e-commerce image-text retrieval

Abstract

Vision-language pre-trained (VLP) models excel in general cross-modal image-text retrieval. However, when fine-tuned for fine-grained e-commerce retrieval, the standard Vision Transformer backbone suffers from channel information equality: it weights all feature channels equally and fails to emphasize critical fine-grained visual cues (e.g., brand logos, materials, textures). This defect reduces cross-modal discriminability, increases hard negative confusion, and limits retrieval accuracy. To solve this problem, we propose GLCE (Global-Local Channel Enhancer), a lightweight plug-and-play module with a dual-branch structure. The global branch captures long-range channel semantic co-occurrence through a strict information bottleneck, while the local branch models fine-grained channel interactions via dimension-preserving 1D convolution. When integrated with full fine-tuning, GLCE breaks the performance bottleneck of strong baselines with nearly no extra parameters. Extensive experiments on the MUGE dataset show that our method achieves comprehensive improvements, with a mean recall of 75.90% and a notable boost in top-5 recall. This work validates the effectiveness of fine-grained channel recalibration for VLP domain adaptation, provides a lightweight industrial solution for fine-grained cross-modal retrieval, and offers new insights for channel attention design in Transformer-based vision-language architectures.