PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning

Cross-modal retrieval aims to align different modalities via semantic similarity. However, existing methods often assume that image-text pairs are perfectly aligned, overlooking Noisy Correspondences in real data. These misaligned pairs misguide similarity learning and degrade retrieval performance. Previous methods often rely on coarse-grained categorizations that simply divide data into clean and noisy samples, overlooking the intrinsic diversity within noisy instances. Moreover, they typically apply uniform training strategies regardless of sample characteristics, resulting in suboptimal sample utilization for model optimization. To address the above challenges, we introduce a novel framework, called Pseudo-label Consistency-Guided Sample Refinement (PCSR), which enhances correspondence reliability by explicitly dividing samples based on pseudo-label consistency. Specifically, we first employ a confidence-based estimation to distinguish clean and noisy pairs, then refine the noisy pairs via pseudo-label consistency to uncover structurally distinct subsets. We further proposed a Pseudo-label Consistency Score (PCS) to quantify prediction stability, enabling the separation of ambiguous and refinable samples within noisy pairs. Accordingly, we adopt Adaptive Pair Optimization (APO), where ambiguous samples are optimized with robust loss functions and refinable ones are enhanced via text replacement during training. Extensive experiments on CC152K, MS-COCO and Flickr30K validate the effectiveness of our method in improving retrieval robustness under noisy supervision.

Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching

Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to find the optimal match for each view's text and compute similarity. However, the visual and text embeddings learned through these approaches have limited information capacity and are prone to interference from locally similar negative samples. To address this issue, we argue that the information capacity of embeddings is crucial and propose Dense-to-Sparse Feature Distilled Visual Semantic Embedding (D2S-VSE), which enhances the information capacity of sparse text by leveraging dense text distillation. Specifically, D2S-VSE is a two-stage framework. In the pre-training stage, we align images with dense text to enhance the information capacity of visual semantic embeddings. In the fine-tuning stage, we optimize two tasks simultaneously, distilling dense text embeddings to sparse text embeddings while aligning images and sparse texts, enhancing the information capacity of sparse text embeddings. Our proposed D2S-VSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets, demonstrating its superiority over recent state-of-the-art methods.