Summary of Vcr: Visual Caption Restoration, by Tianyu Zhang et al.
VCR: Visual Caption Restoration
by Tianyu Zhang, Suyuchen Wang, Lu Li, Ge Zhang, Perouz Taslakian, Sai Rajeswar, Jie Fu, Bang Liu, Yoshua Bengio
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 introduces Visual Caption Restoration (VCR), a novel vision-language task that challenges models to restore partially obscured texts within images using pixel-level hints. The task arises from the unique characteristics of text embedded in images, which require alignment with visual and textual modalities. Unlike previous works integrating text into visual question-answering tasks, VCR demands combined information from provided images, context, and subtle cues from masked texts to achieve accurate restoration. The paper develops a pipeline for generating synthetic images for the task using image-caption pairs, allowing control over task difficulty. A dataset called VCR-Wiki is constructed using Wikipedia captions, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Results show that current vision language models significantly lag behind human performance in the VCR task, and fine-tuning on the dataset does not lead to notable improvements. The paper releases VCR-Wiki and data construction code to facilitate future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to restore text that’s hidden or partially covered in pictures. Imagine trying to read a sign that’s been scribbled over with markers – it’s hard, right? That’s the challenge of this task called Visual Caption Restoration (VCR). The researchers found that current AI models aren’t very good at doing this, even when they’re trained on lots of data. They created a new dataset and tools to help make progress in this area. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Question answering
