Summary of Instancecap: Improving Text-to-video Generation Via Instance-aware Structured Caption, by Tiehan Fan et al.
InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption
by Tiehan Fan, Kepan Nan, Rui Xie, Penghao Zhou, Zhenheng Yang, Chaoyou Fu, Xiang Li, Jian Yang, Ying Tai
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel instance-aware structured caption framework, InstanceCap, which enables instance-level and fine-grained video captioning for the first time. The framework relies on an auxiliary model cluster to convert original video into instances, enhancing instance fidelity. The resulting captions are concise yet precise descriptions of the video content. Experimental results show that InstanceCap outperforms previous models in terms of fidelity and accuracy, while reducing hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to describe videos by breaking them down into smaller parts called “instances.” This helps make the description more accurate and detailed. The researchers also created a big dataset of video-caption pairs to train their model. They tested their method and found that it worked better than other methods at generating accurate and realistic videos. |
