Summary of An Evaluation Framework For Product Images Background Inpainting Based on Human Feedback and Product Consistency, by Yuqi Liang et al.
An Evaluation Framework for Product Images Background Inpainting based on Human Feedback and Product Consistency
by Yuqi Liang, Jun Luo, Xiaoxi Guo, Jianqi Bi
First submitted to arxiv on: 23 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 This paper proposes Human Feedback and Product Consistency (HFPC), an AI-based system to evaluate the quality of generated product images in automated inpainting applications. The current approaches for evaluating product image quality are inconsistent with human feedback, relying on manual annotation, which is time-consuming and prone to errors. HFPC addresses these issues by developing a two-module approach: a reward model trained using multi-modal features from BLIP and comparative learning to assess the appropriateness of backgrounds, and a fine-tuned segmentation model to detect inconsistencies in products. Extensive experiments demonstrate that HFPC achieves state-of-the-art results (96.4% precision) and significantly reduces manual annotation costs. The proposed system can be used for various product advertising applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to judge how good computer-generated images of products are. Right now, people have to look at lots of pictures and label them as good or bad, which is very time-consuming and can be frustrating. The authors came up with a new way to do this that uses special models trained on lots of data. This new method is called HFPC (Human Feedback and Product Consistency). It’s like a referee that looks at the computer-generated images and says “yes” or “no” if they’re good enough. The results are really promising, with accuracy rates above 96%. This could be very useful for companies that want to create fake backgrounds for their products online. |
Keywords
» Artificial intelligence » Multi modal » Precision
