The field of video understanding is moving towards addressing the challenges of domain generalization, with a focus on developing models that can perform well across different domains and environments. This is evident in the introduction of new datasets and benchmarks that test the robustness of models to domain shifts and variations in video content. One of the key areas of research is the development of models that can understand human actions and scenes in diverse environments, including microgravity settings. Additionally, there is a growing interest in improving the performance of models on long videos, with the introduction of new benchmarks and evaluation frameworks. Overall, the field is advancing towards more realistic and challenging video understanding tasks. Noteworthy papers include: VUDG, which proposes a dataset for video understanding domain generalization, and TextVidBench, which introduces a benchmark for long video scene text understanding. Grid-LOGAT is also notable for its grid-based local and global area transcription system for video question answering, and Go Beyond Earth for its introduction of a benchmark for spatio-temporal and semantic understanding of human activities in microgravity.