LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos

Despite rapid progress in Multi-modal Large Language Mod-els (MLLMs), understanding long-form videos is still bottlenecked bylimited context windows. While recent keyframe sampling methods at-tempt to mitigate this by distilling video inputs into a compact set ofquery-relevant frames, navigating the vast spatio-temporal search spaceremains challenging, as spatial detail and temporal coverage often conflict.To address this, we introduce LENS, a training-free keyframe samplingframework that dynamically decides when to zoom in for fine-graineddetails and when to zoom out for broader context based on the textquery. Concretely, LENS adaptively allocates a limited frame budgetbetween spatial zoom-ins, which highlight query-relevant regions withinindividual frames, and temporal zoom-outs, which expand the temporalscope through multi-frame aggregation, enabling the model to reasonacross multiple granularities while capturing both high-fidelity details andlong-range context. Across diverse long-form video benchmarks, LENSconsistently outperforms prior state-of-the-art keyframe sampling meth-ods and delivers substantial gains over uniform sampling, improvingVideo-MME accuracy from 53.3% to 60.7% with Qwen2.5-VL. Code isavailable at https://github.com/zhangce01/LENS.