Summary of Human-guided Image Generation For Expanding Small-scale Training Image Datasets, by Changjian Chen et al.
Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets
by Changjian Chen, Fei Lv, Yalong Guan, Pengcheng Wang, Shengjie Yu, Yifan Zhang, Zhuo Tang
First submitted to arxiv on: 22 Dec 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 paper introduces a human-guided image generation method to expand computer vision datasets. This approach addresses the limitations of existing generative models by allowing users to control the generated images through multi-modal projections and sample-level prompt refinement. The paper’s methodology enables users to refine prompts and re-generate images for better performance, making it easier for novice users to provide feedback. The proposed method is demonstrated to be effective in improving model performance in classification and object detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with computer vision models. These models are limited because they don’t have enough pictures to learn from. To fix this, scientists came up with an idea to use pre-trained generative models to create more images. However, these generated images aren’t always great and some might be unwanted. The proposed method lets humans control the image generation process to get better results. It works by allowing users to explore and refine prompts and re-generate images until they’re satisfied. |
Keywords
» Artificial intelligence » Classification » Image generation » Multi modal » Object detection » Prompt
