Contrastive Learning-Based Fine-Tuning Method for Cross-Modal Text-Image Retrieval

With the rapid proliferation of social media and smart devices, multimodal data has grown explosively, making traditional unimodal retrieval methods insufficient for addressing cross-modal semantic correlation tasks. To tackle the challenges caused by text redundancy and image noise in real-world scenarios, this paper proposes a contrastive learning-based, two-stage progressive fine-tuning approach for building a high-precision text-image cross-modal retrieval system. We design an efficient data preprocessing pipeline: Text data undergoes tokenization, stop-word filtering, and TF-IDF-based keyword extraction, while image data is enhanced using Cutout-style random masking to improve robustness against occlusion and noise. The model employs a dual-tower architecture composed of a ResNet50 visual encoder and a RoBERTa-based text encoder, with joint embedding space optimized using InfoNCE loss. A Locked-image Tuning (LiT) strategy is introduced, where the visual encoder is initially frozen and then both encoders are fine-tuned jointly with mixed-precision training and gradient clipping to ensure convergence stability. To improve data loading efficiency, we utilize LMDB to store 50,000 image-text pairs, significantly reducing I/O overhead. Experiments on an industry-scale dataset demonstrate that the fine-tuned model achieves R@5 of 87.1% (text-to-image) and 87.4% (image-to-text), outperforming baselines by over 13% while reducing GPU memory usage by 18%. Our method achieves a balance between accuracy, efficiency, and scalability, making it suitable for applications such as social media content management and e-commerce cross-modal search.