Energy vs. Noise: Towards Robust Temporal Action Localization in Open-World

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.