Video ReCap: Recursive Captioning of Hour-Long Videos
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Cited by 20 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g., objects, scenes, atomic actions). However, most real-world videos last for minutes or hours and have a complex hierarchical structure spanning different temporal granularities. We propose Video ReCap, a recursive video captioning model that can process video inputs of dramatically different lengths (from 1 second to 2 hours) and output video captions at multiple hierarchy levels. The recursive video-language architecture exploits the synergy between different video hierarchies and can process hour-long videos efficiently. We utilize a curriculum learning training scheme to learn the hierarchical structure of videos, starting from clip-level captions describing atomic actions, then focusing on segment-level descriptions, and concluding with generating summaries for hour-long videos. Furthermore, we introduce Ego4D-HCap dataset by augmenting Ego4D with 8,267 manually collected long-range video summaries. Our recursive model can flexibly generate captions at different hierarchy levels while also being useful for other complex video understanding tasks, such as VideoQA on EgoSchema. Data, code, and models are available at: https://sites.google.com/view/vidrecap
Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). Builds on 51 of its 79 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
51 of 79 references.
References · 51
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38 more
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- 2017Dense-Captioning Events in Videoscited by 789
- 2019VideoBERT: A Joint Model for Video and Language Representation Learningcited by 702
- 2023EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understandingcited by 666
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- 2021ClipCap: CLIP Prefix for Image Captioningcited by 557
- 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 483
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- 2023Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioningcited by 263
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- 2022Egocentric Video-Language Pretrainingcited by 231
- 2022Zero-Shot Video Question Answering via Frozen Bidirectional Language Modelscited by 229
- 2022Learning Video Representations from Large Language Modelscited by 204
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- 2023EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbonecited by 130
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- 2020MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioningcited by 79
- 2020Multimodal Pretraining for Dense Video Captioningcited by 79
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Citing papers per year: 2024: 22; 2025: 48; 2026: 18.
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