Summary of Text Embedding Is Not All You Need: Attention Control For Text-to-image Semantic Alignment with Text Self-attention Maps, by Jeeyung Kim et al.
Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps
by Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
First submitted to arxiv on: 21 Nov 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 explores the relationship between text-to-image diffusion models and their ability to generate accurate images based on given prompts. It focuses on the cross-attention maps that indicate which specific regions of an image are attended by each token in the prompt. By analyzing these maps, researchers can identify patterns and issues that lead to generation flaws such as missing objects or incorrect attribute binding. The study highlights two key observations: (1) similar text embeddings between tokens can cause their cross-attention maps to focus on the same regions, leading to inaccuracies; and (2) text embeddings often fail to capture syntactic relations within text attention maps, resulting in oversight of these relationships in cross-attention modules. To address this, the authors propose a method that transfers syntactic relations from text attention maps to cross-attention modules during test-time optimization. This approach leverages inherent information within text attention maps to improve image-text semantic alignment across diverse prompts without relying on external guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computer programs can create images based on words. It looks at a special map that shows which parts of an image are important for each word in the prompt. By studying these maps, researchers found that some problems occur when creating images. They identified two main issues: (1) similar words in the prompt can cause the program to focus on the same part of the image; and (2) the program doesn’t always understand how words relate to each other, which leads to mistakes. To fix this, the authors came up with a new way to use the information from these word maps to improve the image-creation process. |
Keywords
» Artificial intelligence » Alignment » Attention » Cross attention » Diffusion » Optimization » Prompt » Token
