Think-Clip-Sample: Slow-Fast Frame Selection for Video Understanding
This work addresses efficiency and accuracy challenges for researchers and practitioners using MLLMs in long video analysis, though it is incremental as it builds on existing MLLM frameworks.
The paper tackles the problem of limited performance in multi-modal large language models for long-form video understanding due to computational constraints and suboptimal frame selection, resulting in up to 6.9% accuracy improvement and 50% reduction in inference time.
Recent progress in multi-modal large language models (MLLMs) has significantly advanced video understanding. However, their performance on long-form videos remains limited by computational constraints and suboptimal frame selection. We present Think-Clip-Sample (TCS), a training-free framework that enhances long video understanding through two key components: (i) Multi-Query Reasoning, which generates multiple queries to capture complementary aspects of the question and video; and (ii) Clip-level Slow-Fast Sampling, which adaptively balances dense local details and sparse global context. Extensive experiments on MLVU, LongVideoBench, and VideoMME demonstrate that TCS consistently improves performance across different MLLMs, boosting up to 6.9% accuracy, and is capable of achieving comparable accuracy with 50% fewer inference time cost, highlighting both efficiency and efficacy of TCS on long video understanding.