Accurate and Lightweight Learning for Specific Domain Image-Text Retrieval

Recent advances in vision-language pre-trained models like CLIP have greatly enhanced general domain image-text retrieval performance. This success has led scholars to develop methods for applying CLIP to Specific Domain Image-Text Retrieval (SDITR) tasks such as Remote Sensing Image-Text Retrieval (RSITR) and Text-Image Person Re-identification (TIReID). However, these methods for SDITR often neglect two critical aspects: the enhancement of modal-level distribution consistency within the retrieval space and the reduction of CLIP's computational cost during inference. To address these issues, this paper presents a novel framework, Accurate and lightweight learning for specific domain Image-text Retrieval (AIR), based on the CLIP. AIR incorporates a Modal-Level distribution Consistency Enhancement regularization (MLCE) loss and a Self-Pruning Distillation Strategy (SPDS) to improve retrieval precision and computational efficiency. The MLCE loss harmonizes the sample distance distributions within image and text modalities, fostering a retrieval space closer to the ideal state. SPDS employs a strategic knowledge distillation process to transfer deep multimodal insights from CLIP to a shallower level, maintaining only the essential layers for inference, thus achieving model light-weighting. Comprehensive experiments across various datasets in RSITR and TIReID reveal that MLCE loss secures optimal retrieval, while SPDS achieves a favorable balance between accuracy and computational demand during testing.