Temporal Action Localization (TAL) aims to accurately identify the start and end times of actions in untrimmed videos and classify them according to specific labels. However, the complexity and imbalance between target actions and background in video data make this task particularly challenging. Although relying on large amounts of finely annotated data has led to some progress in existing methods, the presence of noisy labels in large-scale annotations limits their application in open-world scenarios. To address this issue, we take the perspective of the data itself, modeling the different energy patterns exhibited by the action foreground and background in video data to enhance video content inference. Specifically, we propose the Energy-Driven Meta Purifier (EDMP) method, which utilizes a meta-learning training paradigm to avoid dependence on extensive and precise manual annotations. Under this pipeline, we use energy modeling to distinguish between different actions and backgrounds from the perspective of energy differences, thereby improving the model's robustness to category noise. Additionally, these energy-based distinctions are employed to further refine action boundaries, enhancing the model's robustness to boundary noise. Experiments on THUMOS14 and ActivityNet1.3 datasets show that EDMP effectively enhances the robustness of TAL models.

Cross-modal retrieval with noisy correspondences is a critical challenge, especially when data annotations for large-scale multimodal datasets are prone to systematic corruption. To mitigate the impact of noise, many existing methods rely on small-loss sample selection to filter out clean samples. However, these methods can ineluctably result in the inclusion of false positives, which significantly degrade the performance. To tackle this issue, we propose a novel method, named the Meta Similarity Importance Assignment Network (MSIAN), to achieve robust cross-modal retrieval. MSIAN employs a meta-learning strategy to dynamically learn the importance of each sample through a two-level optimization process. With adaptively guiding the learning process, MSIAN adjusts the importance weight of each sample based on its inherent trustworthiness. Thereby, thus iterative mechanism progressively shifts the network's focus on the most reliable data points, amplifying the impact of credible samples while diminishing the adaptive weight of noisy ones. Furthermore, MSIAN dynamically adapts the soft margin of each sample through continuously updated adaptive weights, thereby improving the robustness of the model. Extensive experiments on three widely used datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the effectiveness of our approach in improving cross-modal retrieval performance.