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Localizing Moments in Video with Natural Language

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arxiv 1708.01641 v1 pith:JBSARKAE submitted 2017-08-04 cs.CV

Lisa Anne Hendricks , Oliver Wang , Eli Shechtman , Josef Sivic , Trevor Darrell , Bryan Russell This is my paper · ORCID
classification cs.CV
keywords videolanguagenaturalmomentmomentsdidemoexpressionslocalized
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We consider retrieving a specific temporal segment, or moment, from a video given a natural language text description. Methods designed to retrieve whole video clips with natural language determine what occurs in a video but not when. To address this issue, we propose the Moment Context Network (MCN) which effectively localizes natural language queries in videos by integrating local and global video features over time. A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment. Therefore, we collect the Distinct Describable Moments (DiDeMo) dataset which consists of over 10,000 unedited, personal videos in diverse visual settings with pairs of localized video segments and referring expressions. We demonstrate that MCN outperforms several baseline methods and believe that our initial results together with the release of DiDeMo will inspire further research on localizing video moments with natural language.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

    cs.CV 2026-07 conditional novelty 7.0 of 10

    TimeLens2 shows that a compact video MLLM can localize multiple evidence intervals in long videos by training on verified interval labels and a Wasserstein-based time-distance reward.

  2. VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos

    cs.CV 2025-06 conditional novelty 7.0 of 10

    VidEvent provides a large annotated video dataset for extracting structured event scripts and predicting future events, with baseline benchmarks.

  3. Do Language Models Understand Time?

    cs.CV 2024-12 conditional novelty 3.0 of 10

    A survey arguing that video-LLMs rely on pretrained encoders and short-biased datasets, leaving them weak at long-term temporal reasoning such as causality and event progression.

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