Summary of Progressive Compositionality in Text-to-image Generative Models, by Xu Han et al.
Progressive Compositionality In Text-to-Image Generative Models
by Xu Han, Linghao Jin, Xiaofeng Liu, Paul Pu Liang
First submitted to arxiv on: 22 Oct 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 The paper introduces a novel approach to generating high-quality complex contrastive images that can be directly discriminated by diffusion models based on visual representations. The authors leverage large-language models (LLMs) to compose realistic, complex scenarios and combine them with Visual-Question Answering (VQA) systems to create a contrastive dataset called ConPair. This dataset consists of 15k pairs of high-quality contrastive images featuring minimal visual discrepancies and covers a wide range of attribute categories, including complex and natural scenarios. The authors also propose EvoGen, a multi-stage curriculum for contrastive learning of diffusion models that learns effectively from error cases, or hard negative images. Experimental results demonstrate the effectiveness of the proposed framework on compositional text-to-image (T2I) benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer systems better at creating realistic images by combining two ideas: big language models and visual question answering. The authors create a special dataset called ConPair with 15,000 pairs of images that are similar but not exactly the same. They use this dataset to train a new way for computers to learn how to generate more complex images. This is important because it can help computers better understand what makes an image realistic or not. |
Keywords
» Artificial intelligence » Diffusion » Question answering
