Summary of Dibs: Enhancing Dense Video Captioning with Unlabeled Videos Via Pseudo Boundary Enrichment and Online Refinement, by Hao Wu et al.
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement
by Hao Wu, Huabin Liu, Yu Qiao, Xiao Sun
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Dive Into the BoundarieS (DIBS) framework is a novel pretraining method for dense video captioning (DVC), which aims to improve the quality of generated event captions and their associated pseudo boundaries from unlabeled videos. By leveraging large language models, DIBS generates diverse caption candidates and optimizes pseudo boundaries under objectives such as diversity, event-centricity, temporal ordering, and coherence. The framework also includes an online boundary refinement strategy that iteratively improves pseudo boundary quality during training. Experiments demonstrate the effectiveness of DIBS components on standard DVC datasets like YouCook2 and ActivityNet, outperforming state-of-the-art Vid2Seq across many metrics using just 0.4% of unlabeled video data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DIBS is a new way to improve videos by adding words that describe what’s happening in them. It uses big computers to look at lots of videos and generate text that describes the actions taking place. The goal is to make the descriptions really good and accurate, which can be useful for things like searching through video libraries or creating video summaries. |
Keywords
» Artificial intelligence » Pretraining
