Summary of You Only Submit One Image to Find the Most Suitable Generative Model, by Zhi Zhou et al.
You Only Submit One Image to Find the Most Suitable Generative Model
by Zhi Zhou, Lan-Zhe Guo, Peng-Xiao Song, Yu-Feng Li
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Generative Model Identification (GMI) setting aims to efficiently identify the most suitable generative models for user requirements from a large pool of candidate models. This is achieved through a novel framework consisting of three modules: a weighted Reduced Kernel Mean Embedding (RKME) framework, a pre-trained vision-language model, and an image interrogator. The GMI setting addresses dimensionality challenges, cross-modality issues, and enables users to describe their requirements with a single example image, resulting in high top-4 identification accuracy of over 80%. This comprehensive solution demonstrates the effectiveness and efficiency of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help people find the best generative models for their needs. These models can create new images based on examples. Right now, it’s hard to search for these models because there aren’t good ways to organize them. The authors suggest a three-part solution that makes it easier to find the right model by understanding what kind of images the user wants and how they relate to each other. They show that their approach works well, with users only needing to provide one example image to get accurate results. |
Keywords
» Artificial intelligence » Embedding » Generative model » Language model
