The field of video understanding is moving towards more efficient and effective methods for analyzing and interpreting video data. One notable trend is the use of large language models to improve performance in tasks such as long-term action anticipation and video question answering. Another area of focus is the development of novel frameworks and methods for cross-video understanding, which enables the establishment of meaningful connections across multiple video streams. Additionally, there is a growing interest in designing more efficient and token-effective approaches for video question answering, which can reduce the computational cost and improve the accuracy of existing models. Noteworthy papers in this area include:

• Bidirectional Action Sequence Learning for Long-term Action Anticipation with Large Language Models, which proposes a method that combines forward prediction with backward prediction using a large language model.
• Enhancing Long Video Question Answering with Scene-Localized Frame Grouping, which introduces a novel method called SLFG that combines individual frames into semantically coherent scene frames.
• VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering, which presents a framework that addresses the challenges of cross-video question answering through person-anchored hierarchical reasoning.
• Less is More: Token-Efficient Video-QA via Adaptive Frame-Pruning and Semantic Graph Integration, which proposes a novel post-processing method that intelligently prunes the selected keyframes and introduces a lightweight semantic graph to provide critical context.
• Gather and Trace: Rethinking Video TextVQA from an Instance-oriented Perspective, which proposes a novel model termed GAT that obtains accurate reading results for each video text instance and captures dynamic evolution of text in the video flow.