Summary of Ltos: Layout-controllable Text-object Synthesis Via Adaptive Cross-attention Fusions, by Xiaoran Zhao et al.
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions
by Xiaoran Zhao, Tianhao Wu, Yu Lai, Zhiliang Tian, Zhen Huang, Yahui Liu, Zejiang He, Dongsheng Li
First submitted to arxiv on: 21 Apr 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 Medium Difficulty Summary: Controllable text-to-image generation has numerous applications, including emoji and poster creation. The paper focuses on a novel task, layout-controllable text-object synthesis (LTOS), which combines text rendering and layout-to-image generation tasks. LTOS aims to generate images with objects and visual text based on predefined object layouts and text contents. To tackle this challenge, the authors construct a dataset containing well-aligned labels of visual text and object information. A proposed framework, called layout-controllable text-object adaptive fusion (TOF), integrates text rendering and object generation modules. TOF uses self-adaptive cross-attention fusion module to enhance image-text integration by controlling the influence of cross-attention outputs on image generation. Experimental results demonstrate that the method outperforms state-of-the-art models in LTOS, text rendering, and layout-to-image tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine being able to generate cool images with custom text and objects! This paper explores a new way to do this by combining two existing techniques: generating text and objects. The authors create a special dataset for this task and design a system that can combine text and objects in an image. They test their approach and show it works better than other methods. This research has the potential to make creating custom images with text and objects easier and more accurate. |
Keywords
» Artificial intelligence » Cross attention » Image generation
