The field of temporal action localization and video understanding is moving towards more innovative and effective methods for recognizing and detecting actions in videos. Recent developments have focused on leveraging textual information, such as semantic descriptions and natural language queries, to improve the accuracy and robustness of action localization models. Additionally, there is a growing interest in developing frameworks that can handle open-world scenarios, where the model needs to detect actions and events in unseen data. Noteworthy papers in this area include Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization, which proposes a novel few-shot learning framework that leverages textual semantic information to enhance action localization performance. Grounding-MD is another significant contribution, presenting a grounded video-language pre-training framework tailored for open-world moment detection. Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer is also notable, as it proposes a novel two-branch framework that bridges the gap between weakly and fully-supervised temporal action localization.