Summary of Token Merging For Training-free Semantic Binding in Text-to-image Synthesis, by Taihang Hu et al.
Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis
by Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao, Fahad Shahbaz Khan, Jian Yang, Ming-Ming Cheng, Kai Wang, Yaxing Wang
First submitted to arxiv on: 11 Nov 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 paper proposes a novel method called Token Merging (ToMe) to improve semantic binding in text-to-image models. Semantic binding refers to the task of associating objects with their attributes or linking them to other related sub-objects. ToMe enhances semantic binding by aggregating relevant tokens into a single composite token, ensuring that all objects and attributes share the same cross-attention map. The approach also incorporates two auxiliary losses to refine the generation integrity in the initial stages. Experiments on the T2I-CompBench and GPT-4o object binding benchmark show the effectiveness of ToMe in complex scenarios involving multiple objects and attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make text-to-image models better at recognizing relationships between words. Right now, these models can generate images that look like they were taken from real-life scenes, but they often struggle to connect different objects or ideas together. The authors of the paper propose a technique called Token Merging (ToMe) that helps solve this problem by combining similar words into a single unit. They also add some extra steps to make sure the generated images are more accurate and detailed. The results show that ToMe is really good at handling complex scenes with many objects and ideas. |
Keywords
» Artificial intelligence » Cross attention » Gpt » Token
