Summary of Reframing Image Difference Captioning with Blip2idc and Synthetic Augmentation, by Gautier Evennou et al.
Reframing Image Difference Captioning with BLIP2IDC and Synthetic Augmentation
by Gautier Evennou, Antoine Chaffin, Vivien Chappelier, Ewa Kijak
First submitted to arxiv on: 20 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 The proposed framework adapts existing image captioning models to the Image Difference Captioning (IDC) task while augmenting IDC datasets at low computational cost. The BLIP2IDC model outperforms two-stream approaches by a significant margin on real-world IDC datasets. Synthetic augmentation is also introduced to improve IDC model performance in an agnostic fashion, creating a new challenging dataset named Syned1. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a simple yet effective framework to adapt image captioning models to the IDC task and augment IDC datasets. The BLIP2IDC model performs well on real-world images, while synthetic augmentation improves IDC model performance without requiring additional data. |
Keywords
» Artificial intelligence » Image captioning
