The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval

• URL: http://arxiv.org/abs/2406.18113v3
• Date: Mon, 14 Oct 2024 06:50:19 GMT
• Title: The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval
• Authors: Boris Meinardus, Anil Batra, Anna Rohrbach, Marcus Rohrbach,
• Abstract summary: Video-language tasks necessitate spatial and temporal comprehension and require significant compute. This work demonstrates the surprising effectiveness of leveraging image-text pretrained MLLMs for moment retrieval. We achieve a new state-of-the-art in moment retrieval on the widely used benchmarks Charades-STA, QVHighlights, and ActivityNet Captions.
• Score: 36.516226519328015
• License:
• Abstract: Recent studies have shown promising results in utilizing multimodal large language models (MLLMs) for computer vision tasks such as object detection and semantic segmentation. However, many challenging video tasks remain under-explored. Video-language tasks necessitate spatial and temporal comprehension and require significant compute. Therefore, prior works have developed complex, highly specialized architectures or leveraged additional input signals such as video transcripts to best encode contextual and temporal information, which limits their generality and can be impractical. One particularly challenging task is video moment retrieval, which requires precise temporal and contextual grounding. This work demonstrates the surprising effectiveness of leveraging image-text pretrained MLLMs for moment retrieval. We introduce Mr. BLIP (Mr. as in Moment Retrieval), a multimodal, single-stage model that requires no expensive video-language pretraining, no additional input signal (e.g., no transcript or audio), and has a simpler and more versatile design than prior state-of-the-art methods. We achieve a new state-of-the-art in moment retrieval on the widely used benchmarks Charades-STA, QVHighlights, and ActivityNet Captions. Notably, we attain over 9% (absolute) higher Recall (at 0.5 and 0.7 IoU) on the challenging long-video multi-moment QVHighlights benchmark. Our code is publicly available.

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