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Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models

Paper · 2026 · Beibei Zhang, Yanan Lu, Lin Fen et al.

Summary

Video Speaking Style Recognition (VSSR) assigns a conversation video to a category such as Explain or Threaten. Recent training-free LLM approaches caption video frames and give the captions together with the dialogue to an LLM. The authors argue this is inefficient: talking-head footage is highly repetitive and tends to suggest neutral styles, which drowns out the more informative dialogue and the few visual cues that matter.

They propose CFMiS, a training-free framework that selects information in two stages. First, a text-dominant modality selection step checks whether a prediction based only on the ASR transcript is reliable. It then asks whether a video summary would add anything, and chooses text, vision, or both. Second, if vision is kept, a visual refinement step scores clips for relevance to the style query, samples extra frames from the top clips, and adds clip summaries one at a time until the LLM is confident.

Across several datasets, CFMiS beats all training-free baselines and most trained models. This shows that filtering redundant input helps LLM-based multimodal reasoning.

Key points

  • Pipeline: audio is extracted with FFmpeg and transcribed by an ASR model. The video is split into clips, and a VLM captions each clip's middle frame. The best captioner tested was CogAgent.
  • Raw frames vs. captions: feeding raw frames directly to GPT-4o Mini performed worse than converting the visual content to language.
  • Clip limit: at most N_M = 5 relevant clips are considered, because adding more irrelevant clips increases interference.
  • LLM ablation on LVU-VSSR: reading the table, GPT-4o scored best (about 55.2 Acc and 56.0 WF1), and GPT-4o Mini, the default model, reached about 53.7 Acc. Most proprietary models outperformed open-source ones such as LLaMA3, Qwen3-32B and DeepSeek-R1, with GPT-3.5 as the exception.
  • Limitations: summarization and CoT outputs add latency and cost, and LLM hallucinations can distort the relevance scores.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢TencentShenzhen · China163 works · 21,032 citations🎓Nanjing UniversityNanjing · China99 works · 8,510 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 811 works

Cite

Cite as Beibei Zhang, Yanan Lu, Lin Fen and Tongwei Ren. 2026. Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1466

@inproceedings{zhang2026coarse,
  title = {{Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models}},
  author = {Beibei Zhang, Yanan Lu, Lin Fen and Tongwei Ren},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1466},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1466}
}

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