The field of video understanding is rapidly advancing, with a focus on developing innovative methods to process and analyze long video streams. Recent developments have centered around improving the efficiency and accuracy of video understanding models, particularly in terms of capturing temporal context and preserving spatial information. Notable advancements include the use of vision foundation models, state space prompting, and graph-based retrieval-reasoning-augmented generation frameworks. These approaches have shown significant improvements in performance on various video understanding benchmarks.

Some noteworthy papers in this area include: Video-SALMONN S, which proposes a streaming audio-visual LLM that can process 3-hour videos at 1 FPS and 360p resolution under a fixed memory budget. VideoLucy, a deep memory backtracking framework for long video understanding that employs a hierarchical memory structure with progressive granularity. Vgent, a novel graph-based retrieval-reasoning-augmented generation framework that enhances LVLMs for long video understanding. Efficient Video Sampling, a simple method for reducing token redundancy in videos by identifying and pruning temporally static patches.