@inproceedings{wang-etal-2026-efficient-frame,
title = "Efficient Frame Selection for Long Videos at Test Time with Attention-Based {MLLM} Selectors",
author = "Wang, Yilin and
Zheng, Xiangxi and
Mao, Dongxing and
Li, Linjie and
Yang, Zhengyuan and
Yu, Ping and
Yan, Rui and
Yao, Yuan and
Wang, Alex Jinpeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1893/",
pages = "40328--40341",
ISBN = "979-8-89176-462-0",
abstract = "Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames seemingly requires understanding the video first. We resolve this circular dependency with a simple observation: cross-modal attention at validation-selected extraction layers in MLLMs already provides query-relevant frame evidence without requiring autoregressive generation. We exploit this property to build a training-free frame selector. A lightweight MLLM selector, even with only 2B parameters, can extract frame-level evidence by converting selected-layer attention into relevance scores through query-conditioned aggregation. This enables cross-frame comparison without autoregressive decoding. To handle the selector{'}s own context constraint, we formulate the joint allocation of candidate pool size and per-frame token budget as a discrete optimization problem solved by dynamic programming. Under a 32-frame budget, our selector improves over uniform sampling by up to 6.4 points on Video-MME and outperforms prior training-based selectors under matched frame budgets, while generalizing across selector, answerer backbones, and tasks without retraining."
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<abstract>Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames seemingly requires understanding the video first. We resolve this circular dependency with a simple observation: cross-modal attention at validation-selected extraction layers in MLLMs already provides query-relevant frame evidence without requiring autoregressive generation. We exploit this property to build a training-free frame selector. A lightweight MLLM selector, even with only 2B parameters, can extract frame-level evidence by converting selected-layer attention into relevance scores through query-conditioned aggregation. This enables cross-frame comparison without autoregressive decoding. To handle the selector’s own context constraint, we formulate the joint allocation of candidate pool size and per-frame token budget as a discrete optimization problem solved by dynamic programming. Under a 32-frame budget, our selector improves over uniform sampling by up to 6.4 points on Video-MME and outperforms prior training-based selectors under matched frame budgets, while generalizing across selector, answerer backbones, and tasks without retraining.</abstract>
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%0 Conference Proceedings
%T Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors
%A Wang, Yilin
%A Zheng, Xiangxi
%A Mao, Dongxing
%A Li, Linjie
%A Yang, Zhengyuan
%A Yu, Ping
%A Yan, Rui
%A Yao, Yuan
%A Wang, Alex Jinpeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-efficient-frame
%X Understanding long videos with multimodal large language models (MLLMs) requires selecting a compact set of frames from thousands of candidates, yet identifying the right frames seemingly requires understanding the video first. We resolve this circular dependency with a simple observation: cross-modal attention at validation-selected extraction layers in MLLMs already provides query-relevant frame evidence without requiring autoregressive generation. We exploit this property to build a training-free frame selector. A lightweight MLLM selector, even with only 2B parameters, can extract frame-level evidence by converting selected-layer attention into relevance scores through query-conditioned aggregation. This enables cross-frame comparison without autoregressive decoding. To handle the selector’s own context constraint, we formulate the joint allocation of candidate pool size and per-frame token budget as a discrete optimization problem solved by dynamic programming. Under a 32-frame budget, our selector improves over uniform sampling by up to 6.4 points on Video-MME and outperforms prior training-based selectors under matched frame budgets, while generalizing across selector, answerer backbones, and tasks without retraining.
%U https://aclanthology.org/2026.findings-emnlp.1893/
%P 40328-40341
Markdown (Informal)
[Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors](https://aclanthology.org/2026.findings-emnlp.1893/) (Wang et al., Findings 2026)
ACL
- Yilin Wang, Xiangxi Zheng, Dongxing Mao, Linjie Li, Zhengyuan Yang, Ping Yu, Rui Yan, Yuan Yao, and Alex Jinpeng Wang. 2026. Efficient Frame Selection for Long Videos at Test Time with Attention-Based MLLM Selectors. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40328–40341, Budapest, Hungary. Association for Computational Linguistics.