Self-Supervised Pre-Training With Adaptive Sampling for Fine-Grained Image Retrieval

Fine-grained image retrieval (FGIR) is a challenging task that demands precise representation of subtle inter-class variations and intra-class similarities. Traditional methods, heavily reliant on labeled data, face scalability and cost-efficiency challenges, limiting their applicability in real-world scenarios. To address these limitations, this study introduces a novel self-supervised pre-training framework tailored for FGIR. Central to our approach is the adaptive sample selector module, which dynamically selects training samples of varying difficulties, enhancing the learning of discriminative features without significantly increasing training costs. Additionally, we propose an integrated learning strategy that synergistically combines contrastive and generative learning, enabling the model to capture both intra-image contextual information and inter-image similarities. This approach significantly improves feature extraction, setting a new benchmark for FGIR tasks. Extensive experiments on multiple FGIR benchmarks demonstrate that our model achieves state-of-the-art performance, consistently surpassing existing methods. By reducing reliance on labeled data and advancing self-supervised learning for fine-grained image analysis, this framework offers a scalable and efficient solution for FGIR and beyond.