Summary of Evaluating Text-to-visual Generation with Image-to-text Generation, by Zhiqiu Lin and Deepak Pathak and Baiqi Li and Jiayao Li and Xide Xia and Graham Neubig and Pengchuan Zhang and Deva Ramanan
Evaluating Text-to-Visual Generation with Image-to-Text Generation
by Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, Deva Ramanan
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); 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 proposed VQAScore metric uses a visual-question-answering (VQA) model to evaluate the alignment between generated images and text prompts. This addresses limitations in existing metrics like CLIPScore, which can conflate complex prompts. The VQAScore is computed using off-the-shelf models or an in-house model that follows best practices, such as a bidirectional image-question encoder. The proposed metric outperforms strong baselines on various benchmarks, including 8 image-text alignment datasets. Furthermore, VQAScore can align text with video and 3D models. This allows researchers to benchmark text-to-visual generation using complex texts that capture compositional structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to measure how well AI-generated images match what people are describing in words. Currently, it’s hard to tell if an image is really like the description because there aren’t good ways to compare them. The authors create a new metric called VQAScore that uses questions to figure out if an image matches a text prompt. This helps fix some problems with old metrics like CLIPScore. The new way of measuring works well and can even be used to compare images with videos or 3D models. This will help researchers make better AI-generated images that are more like what people want. |
Keywords
» Artificial intelligence » Alignment » Encoder » Prompt » Question answering
