The recent developments in video understanding and action recognition have seen a shift towards more efficient and scalable models, leveraging advancements in both temporal and spatial feature extraction. Researchers are increasingly focusing on integrating long-range dependencies and higher-order correlations to enhance the robustness and accuracy of action classification. Notably, the use of hypergraph transformers and autoregressive models has shown promise in capturing complex contextual features, outperforming traditional graph convolutional networks. Additionally, there is a growing interest in self-supervised learning approaches that allow models to learn from raw video data without the need for extensive manual annotations. These methods, such as Moving Off-the-Grid, demonstrate the potential for creating more flexible and scene-consistent video representations. Furthermore, the field is witnessing innovations in weakly-supervised learning frameworks, which aim to reduce the dependency on large-scale manual annotations while improving anomaly detection and temporal action localization. The integration of multimodal large language models with video foundation models is also emerging as a powerful tool for enhancing video understanding tasks, offering new paradigms for weakly-supervised learning. Overall, the trend is towards more efficient, scalable, and context-aware models that can handle the complexities of video data while reducing computational and memory demands.