APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval
This addresses a critical bottleneck in video AI for applications requiring analysis of long videos, though it is an incremental improvement over existing training-free methods.
The paper tackles the problem of hour-level long video understanding in multimodal large language models, which struggle with memory and resource constraints, by proposing APVR, a training-free framework that hierarchically retrieves visual information, achieving performance improvements up to 9.7% on benchmarks and state-of-the-art results.
Current multimodal large language models (MLLMs) struggle with hour-level video understanding, facing significant challenges not only in modeling the substantial information volume of long videos but also in overcoming the memory wall and resource constraints during both training and inference. Although recent training-free approaches have alleviated resource demands by compressing visual features, their reliance on incomplete visual information limits the performance potential. To address these limitations, we propose Adaptive Pivot Visual information Retrieval (APVR), a training-free framework that hierarchically retrieves and retains sufficient and important visual information. It breakthroughs the memory wall limitation via two complementary components: Pivot Frame Retrieval employs query expansion and iterative spatio-semantic confidence scoring to identify relevant video frames, and Pivot Token Retrieval performs query-aware attention-driven token selection within up to 1024 pivot frames. This dual granularity approach enables the processing of hour-long videos while maintaining semantic fidelity. Experimental validations on three different baseline MLLMs demonstrate significant performance improvements up to 9.5\%, 4.6\% and 9.7\% on LongVideoBench, VideoMME and MLVU, respectively. APVR achieves state-of-the-art results for both training-free and training-based approaches.