Q-Gate: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding

TL;DR. Q-Gate is a training-free, plug-and-play framework that treats keyframe selection as a dynamic modality-routing problem. Instead of one visual metric or a static fusion of scores, an LLM reads the query and decides, per question, how much to weight three expert streams — suppressing modal noise and knowing when to look versus when to listen.

Training-free
plug-and-play & optimization-free
3 experts
routed per query by an LLM gate
SOTA
on LongVideoBench & Video-MME

Abstract

Long video understanding remains challenging for Multimodal Large Language Models (MLLMs) due to the prohibitive cost of processing dense frame sequences. Prevailing keyframe-selection methods rely on either a single visual-centric metric or a static fusion of heuristic scores. This "one-size-fits-all" paradigm frequently fails: visual-only metrics are ineffective for plot-driven narrative queries, while indiscriminately adding textual scores injects severe modal noise for purely visual tasks.

We propose Q-Gate, which decouples retrieval into three lightweight expert streams — Visual Grounding for local details, Global Matching for scene semantics, and Contextual Alignment for subtitle-driven narratives. A Query-Modulated Gating Mechanism leverages the in-context reasoning of an LLM to assess the query's intent and dynamically allocate attention across the experts, activating necessary modalities while muting irrelevant ones to maximize the signal-to-noise ratio. Extensive experiments on LongVideoBench and Video-MME across multiple MLLM backbones show that Q-Gate substantially outperforms state-of-the-art baselines while remaining optimization-free.

Method

Q-Gate framework overview — **Overview of Q-Gate.** Three parallel expert streams compute time-aligned relevance scores from complementary perspectives — object-level grounding (YOLO-World), global image–text matching (BLIP), and subtitle-driven contextual alignment (Sentence-BERT). A query-aware gate dynamically modulates the streams into a single score distribution; the top-K timestamped frames and subtitles are then passed to a downstream MLLM.

Q-Gate runs in three stages:

🎯

1 · Multi-Granularity Scoring

Three parallel streams produce per-second, time-aligned score distributions, then pass through a unified normalization pipeline (Min-Max scaling + Masked Temperature Softmax, τ = 0.5).

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2 · Query-Aware Gating

An LLM acts as a zero-shot Mixture-of-Experts gate, reading the query and emitting a weight triple that sums to 1 — routing attention to the streams the question actually needs.

🎞️

3 · Sampling & Inference

The fused distribution's top-K seconds become keyframes. Each is anchored with its [Image at MM:SS] timestamp (and subtitles) to form a temporal bridge for the downstream MLLM.

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Training-free & efficient

Off-the-shelf experts, no fine-tuning, a single non-iterative gating pass — adding negligible overhead while drastically cutting the visual token load.

Three Expert Streams

Visual Grounding

YOLO-World

Object-level verification for fine-grained "who / what / where" details — counting, colors, spatial relations.

Global Matching

BLIP

Frame-level semantic similarity between the query and the whole image — the reliable default for scene understanding.

Contextual Alignment

Sentence-BERT

Localizes narrative cues from subtitles — essential for plot-driven, reasoning, and dialogue-anchored queries.

Results

Downstream-QA accuracy (%) on LongVideoBench (LVB) and Video-MME (MME), reported across MLLM backbones and video-length splits (from the paper, Table 1).

Backbone	K	LVB Long	LVB Med	LVB Short	MME Long	MME Med	MME Short
GPT-4o + Q-Gate	8	50.71	56.55	65.41	54.78	59.68	67.16
Qwen3-VL + Q-Gate	32	59.40	63.11	70.59	61.19	66.13	79.41

Q-Gate outperforms strong keyframe-selection baselines in the majority of settings, with the largest gains on long videos (up to +6.4 on Video-MME Long). With only K = 32 frames it rivals or surpasses 72B-scale VLMs and APIs digesting hundreds of frames. See the paper for the full comparison, ablations, and efficiency analysis.

Citation

The paper is accepted to ACM MM 2026; this entry will be updated to the official proceedings citation once available.

@article{wang2026focus,
  title={Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding},
  author={Wang, Shaoguang and Guo, Weiyu and Chen, Ziyang and Hu, Xuming and Xiong, Hui},
  journal={arXiv preprint arXiv:2604.17422},
  year={2026}
}