Summary of Sowing Information: Cultivating Contextual Coherence with Mllms in Image Generation, by Yuhan Pei and Ruoyu Wang and Yongqi Yang and Ye Zhu and Olga Russakovsky and Yu Wu
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation
by Yuhan Pei, Ruoyu Wang, Yongqi Yang, Ye Zhu, Olga Russakovsky, Yu Wu
First submitted to arxiv on: 28 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 proposed Cyclic One-Way Diffusion (COW) framework and its extension, Selective One-Way Diffusion (SOW), leverage the concept of diffusion from physics to tackle challenges in text-vision-to-image generation (TV2I) tasks. By reframing disordered diffusion as a tool for precise information transfer, COW minimizes interference and achieves pixel-level condition fidelity while maintaining visual and semantic coherence. Building on COW, SOW utilizes Multimodal Large Language Models (MLLMs) to clarify semantic and spatial relationships within the image, combining attention mechanisms to dynamically regulate the direction and intensity of diffusion. Experimental results demonstrate the potential of controlled information diffusion for generating more adaptive and versatile images in a learning-free manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a unique idea from physics to make computer vision better. It’s like taking a random walk through an image to get rid of noise and add details. The problem is, this “random walk” can be messy and make the image worse. To fix this, the researchers created two new tools: COW and SOW. COW helps move information around an image in a controlled way, while SOW uses special language models to help understand what’s going on in the image and make it look better. By using these tools together, they can create images that are more detailed and make sense. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image generation
