In recent years, Cross-Modal Retrieval (CMR) has made significant progress in the field of multi-modal analysis. However, since it is time-consuming and labor-intensive to collect large-scale and well-annotated data, the annotation of multi-modal data inevitably contains some noise. This will degrade the retrieval performance of the model. To tackle the problem, numerous robust CMR methods have been developed, including robust learning paradigms, label calibration strategies, and instance selection mechanisms. Unfortunately, they often fail to simultaneously satisfy model performance ceilings, calibration reliability, and data utilization rate. To overcome the limitations, we propose a novel robust cross-modal learning framework, namely Neighbor-aware Instance Refining with Noisy Labels (NIRNL). Specifically, we first propose Cross-modal Margin Preserving (CMP) to adjust the relative distance between positive and negative pairs, thereby enhancing the discrimination between sample pairs. Then, we propose Neighbor-aware Instance Refining (NIR) to identify pure subset, hard subset, and noisy subset through cross-modal neighborhood consensus. Afterward, we construct different tailored optimization strategies for this fine-grained partitioning, thereby maximizing the utilization of all available data while mitigating error propagation. Extensive experiments on three benchmark datasets demonstrate that NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.

In recent years, multi-view learning has aroused extensive research passion. Most existing multi-view learning methods often rely on well-annotations to improve decision accuracy. However, noise labels are ubiquitous in multi-view data due to imperfect annotations. To deal with this problem, we propose a novel noisy label calibration method (NLC) for multi-view classification to resist the negative impact of noisy labels. Specifically, to capture consensus information from multiple views, we employ max-margin rank loss to reduce the heterogeneous gap. Subsequently, we evaluate the confidence scores to enrich predictions associated with noise instances according to all reliable neighbors. Further, we propose Label Noise Detection (LND) to separate multi-view data into a clean or noisy subset, and propose Label Calibration Learning (LCL) to correct noisy instances. Finally, we adopt the cross-entropy loss to achieve multi-view classification. Extensive experiments on six datasets validate that our method outperforms eight state-of-the-art methods.

Multi-view classification aims to leverage information from multiple views of data to improve prediction performance by learning complementary and consistent representations. Therefore, in recent years, multi-view learning has attracted widespread attention in the community. Despite the success of existing multi-view learning methods, there are still some challenges when dealing with large-scale multi-view data. To address this issue, we propose a novel Multi-view Hashing Classification (MHC) framework to encode large-scale multi-view data as binary codes, thereby enhancing the semantic discrimination. Specifically, we leverage class prompts to generate corresponding textual descriptions for each instance and learn the corresponding anchor hash codes. To achieve intra-class compactness and inter-class separability, we propose Class-prompt Contrastive Learning (CCL) to enforce class-wise aggregation and separation in the Hamming space. To mitigate the cross-view heterogeneity gap, we propose a Supervised Cross-view Contrastive (SCC) module to align view-specific hash codes under label supervision. Finally, we present Boundary-aware Independent Hashing (BIH) that introduces boundary-aware constraints to reduce class boundary ambiguity, thereby improving the discrimination of fusion hash codes. Nevertheless, we observe that anchor hash codes could violate the bit independence assumption, which potentially hinders the optimization direction. To this end, we adopt a Bit-level Calibration Mechanism (BCM) to filter out redundant bits, thereby restoring bit independence. Extensive experiments conducted on ten benchmark datasets demonstrate the superiority of the proposed MHC in terms of both classification accuracy and inference efficiency. The code is released at https://github.com/Yuhang-lan04/MHC.