dorsal/arxiv
View SchemaQMAVIS: Long Video-Audio Understanding using Fusion of Large Multimodal Models
| Authors | Zixing Lin, Jiale Wang, Gee Wah Ng, Lee Onn Mak, Chan Zhi Yang Jeriel, Jun Yang Lee, Yaohao Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.06573vv1 |
| URL | https://arxiv.org/abs/2601.06573 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Large Multimodal Models (LMMs) for video-audio understanding have traditionally been evaluated only on shorter videos of a few minutes long. In this paper, we introduce QMAVIS (Q Team-Multimodal Audio Video Intelligent Sensemaking), a novel long video-audio understanding pipeline built through a late fusion of LMMs, Large Language Models, and speech recognition models. QMAVIS addresses the gap in long-form video analytics, particularly for longer videos of a few minutes to beyond an hour long, opening up new potential applications in sensemaking, video content analysis, embodied AI, etc. Quantitative experiments using QMAVIS demonstrated a 38.75% improvement over state-of-the-art video-audio LMMs like VideoLlaMA2 and InternVL2 on the VideoMME (with subtitles) dataset, which comprises long videos with audio information. Evaluations on other challenging video understanding datasets like PerceptionTest and EgoSchema saw up to 2% improvement, indicating competitive performance. Qualitative experiments also showed that QMAVIS is able to extract the nuances of different scenes in a long video audio content while understanding the overarching narrative. Ablation studies were also conducted to ascertain the impact of each component in the fusion pipeline.
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"abstract": "Large Multimodal Models (LMMs) for video-audio understanding have traditionally been evaluated only on shorter videos of a few minutes long. In this paper, we introduce QMAVIS (Q Team-Multimodal Audio Video Intelligent Sensemaking), a novel long video-audio understanding pipeline built through a late fusion of LMMs, Large Language Models, and speech recognition models. QMAVIS addresses the gap in long-form video analytics, particularly for longer videos of a few minutes to beyond an hour long, opening up new potential applications in sensemaking, video content analysis, embodied AI, etc. Quantitative experiments using QMAVIS demonstrated a 38.75% improvement over state-of-the-art video-audio LMMs like VideoLlaMA2 and InternVL2 on the VideoMME (with subtitles) dataset, which comprises long videos with audio information. Evaluations on other challenging video understanding datasets like PerceptionTest and EgoSchema saw up to 2% improvement, indicating competitive performance. Qualitative experiments also showed that QMAVIS is able to extract the nuances of different scenes in a long video audio content while understanding the overarching narrative. Ablation studies were also conducted to ascertain the impact of each component in the fusion pipeline.",
"arxiv_id": "2601.06573",
"authors": [
"Zixing Lin",
"Jiale Wang",
"Gee Wah Ng",
"Lee Onn Mak",
"Chan Zhi Yang Jeriel",
"Jun Yang Lee",
"Yaohao Li"
],
"categories": [
"cs.AI",
"cs.MM"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "QMAVIS: Long Video-Audio Understanding using Fusion of Large Multimodal Models",
"url": "https://arxiv.org/abs/2601.06573",
"version": "v1"
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