MetaSampling: Making Frame Samplers Efficient for Long-Video Question Answering
cs.CV
Submitted: 2026-09-27
Updated: 2026-09-27
License: http://creativecommons.org/licenses/by/4.0/
The gist: Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs).
Terminology
Abstract
Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs). Existing frame-selection methods improve over simple top- k embedding retrieval and uniform sampling, but are typically applied under a fixed global selection budget. We introduce MetaSampling, a training-free, plug-and-play sampling strategy that can be applied on top of existing frame selectors. MetaSampling improves downstream VQA efficiency by dynamically reducing the number of frames passed to the MLLM while preserving, and in some cases improving, answer accuracy. We evaluate MetaSampling across 36 paired frame-selector--MLLM-backbone--VQA-benchmark configurations. MetaSampling reduces the number of selected frames in all 36 configurations and improves accuracy in 25 of them, yielding an average frame reduction of 8.9% while slightly improving accuracy overall.
Sources
- HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering
- Event-Anchored Frame Selection for Effective Long-Video Understanding
- QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
- Query-Conditioned Evidential Keyframe Sampling for MLLM-Based Long-Form Video Understanding
- VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
- Coverage-Driven Adaptive Keyframe Selection for Video Understanding
- Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling
- AdaRD-key: Adaptive Relevance-Diversity Keyframe Sampling for Long-form Video understanding
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