RAVU: Retrieval Augmented Video Understanding with Compositional Reasoning over Graph

Comprehending long videos remains a significant challenge for Large Multi-modal Models (LMMs). Current LMMs struggle to process even minutes to hours videos due to their lack of explicit memory and retrieval mechanisms. To address this limitation, we propose RA VU(R etrieval A ugmented V ideo U nderstanding), a novel framework for video understanding enhanced by retrieval with compositional reasoning over a spatiotemporal graph. We construct a graph representation of the video, capturing both spatial and temporal relationships between entities. This graph serves as a long-term memory, allowing us to track objects and their actions across time. To answer complex queries, we decompose the queries into a sequence of reasoning steps and execute these steps on the graph, retrieving relevant key information. Our approach enables more accurate understanding of long videos, particularly for queries that require multi-hop reasoning and tracking objects across frames. Our approach demonstrate superior performances with limited retrieved frames (5-10) compared with other SOT A methods and baselines on two major video QA datasets, NExT -QA and EgoSchema. 1 Introduction Understanding videos inherently requires the ability to memorize multi-modal information and retrieve it according to a given task. Recent advancements in Large Multi-modal Models (LMMs) have shown promise in tackling this challenge [ Song et al., 2024a; He et al., 2024; Wang et al., 2024a ] .