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From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding
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The integration of Large Language Models (LLMs) with visual encoders has recently shown promising performance in visual understanding tasks, leveraging their inherent capability to comprehend and generate human-like text for visual reasoning. Given the diverse nature of visual data, MultiModal Large Language Models (MM-LLMs) exhibit variations in model designing and training for understanding images, short videos, and long videos. Our paper focuses on the substantial differences and unique challenges posed by long video understanding compared to static image and short video understanding. Unlike static images, short videos encompass sequential frames with both spatial and within-event temporal information, while long videos consist of multiple events with between-event and long-term temporal information. In this survey, we aim to trace and summarize the advancements of MM-LLMs from image understanding to long video understanding. We review the differences among various visual understanding tasks and highlight the challenges in long video understanding, including more fine-grained spatiotemporal details, dynamic events, and long-term dependencies. We then provide a detailed summary of the advancements in MM-LLMs in terms of model design and training methodologies for understanding long videos. Finally, we compare the performance of existing MM-LLMs on video understanding benchmarks of various lengths and discuss potential future directions for MM-LLMs in long video understanding.
Forward citations
Cited by 6 Pith papers
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Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Hierarchical online memory with explicit temporal-causal event edges plus a verify-and-correct agentic reasoner yields large gains on long-form video QA in the streaming setting.
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Empowering Long-form Omni-modal Understanding with Robust Audio Perception
Decoupled audio-visual caption and CoT-QA datasets plus two-stage fine-tuning measurably strengthen auditory perception and cross-modal reasoning in a 7B omni-modal LLM.
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Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
Selecting the visual input that minimizes an MLLM's output entropy (or maximizes its yes/no confidence) improves fine-grained visual search, long-video QA, and temporal grounding without any training.
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Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
Omni-R1 uses GRPO reinforcement learning to train a global reasoning model that selects keyframes and rewrites queries for a detail model, improving video and audio-visual segmentation and out-of-domain QA.
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NeMo: Needle in a Montage for Video-Language Understanding
NeMoBench, an automatically generated benchmark with 31,378 QA pairs, shows that video LLMs struggle with temporal grounding of relevant clips hidden in long montages.
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Infinite Video Understanding
The paper argues that video understanding research should aim at processing streams of arbitrary, unbounded duration and outlines the challenges, directions, and metrics needed.
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