As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely harder to collect and annotate precisely. As an alternative, the co-occurred data pairs (e.g., image-text pairs) collected from the Internet have been widely exploited in the area. Unfortunately, the cheaply collected dataset unavoidably contains many mismatched data pairs, which have been proven to be harmful to the model's performance. To address this, we propose BiCro++ (Improved Bidirectional Cross-modal Similarity Consistency). This module can be integrated into existing cross-modal matching models, enhancing their robustness against noisy data through self-adaptive soft labels that dynamically reflect the true correspondence of data pairs. The basic idea of BiCro++ is motivated by that - taking image-text matching as an example - similar images should have similar textual descriptions and vice versa. This bidirectional similarity consistency can be directly translated into soft labels as a self-supervision signal to train the matching model. To further refine soft label quality, BiCro++ first introduces a Diagonal-Dominance Purification process to identify reliable anchor points from noisy dataset as the reference for soft label estimation. Then it employs a Hybrid-level Codebook Alignment mechanism that establishes enhanced consistency in bidirectional cross-modal similarity. The experiments on three popular cross-modal matching datasets show that our method significantly improves the noise-robustness of various matching models, and surpasses the state-of-the-art method by an average of 5.3%, 3.1% and 6.4% in terms of recall, respectively.

Data augmentation using generative models has emerged as a powerful paradigm for enhancing performance in computer vision tasks. However, most existing augmentation approaches primarily focus on optimizing intrinsic data attributes -- such as fidelity and diversity -- to generate visually high-quality synthetic data, while often neglecting task-specific requirements. Yet, it is essential for data generators to account for the needs of downstream tasks, as training data requirements can vary significantly across different tasks and network architectures. To address these limitations, we propose UtilGen, a novel utility-centric data augmentation framework that adaptively optimizes the data generation process to produce task-specific, high-utility training data via downstream task feedback. Specifically, we first introduce a weight allocation network to evaluate the task-specific utility of each synthetic sample. Guided by these evaluations, UtilGen iteratively refines the data generation process using a dual-level optimization strategy to maximize the synthetic data utility: (1) model-level optimization tailors the generative model to the downstream task, and (2) instance-level optimization adjusts generation policies -- such as prompt embeddings and initial noise -- at each generation round. Extensive experiments on eight benchmark datasets of varying complexity and granularity demonstrate that UtilGen consistently achieves superior performance, with an average accuracy improvement of 3.87\% over previous SOTA. Further analysis of data influence and distribution reveals that UtilGen produces more impactful and task-relevant synthetic data, validating the effectiveness of the paradigm shift from visual characteristics-centric to task utility-centric data augmentation.