Summary of Can Vision-language Models Replace Human Annotators: a Case Study with Celeba Dataset, by Haoming Lu et al.
Can Vision-Language Models Replace Human Annotators: A Case Study with CelebA Dataset
by Haoming Lu, Feifei Zhong
First submitted to arxiv on: 12 Oct 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 investigates the capabilities of Vision-Language Models (VLMs) in image data annotation, comparing their performance on the CelebA dataset with manual annotation. The study finds that LLaVA-NeXT, a state-of-the-art VLM, achieves 79.5% agreement with human annotations on 1000 images. By incorporating re-annotations of disagreed cases, AI annotation consistency improves to 89.1%. Cost assessments show that AI annotation significantly reduces expenses compared to traditional manual methods, representing less than 1% of the costs for manual annotation in CelebA. The findings support VLMs as a viable alternative for specific annotation tasks, reducing financial burden and ethical concerns associated with large-scale manual data annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well AI models can help label images. They compare the AI’s work to what humans do when labeling pictures. The AI model is good at agreeing with human labels most of the time, but not always. By looking closer at the disagreements, they found a way to make the AI more consistent. This new approach makes the AI even better at labeling certain types of images. They also compared how much money it costs to use the AI versus humans and found that the AI is much cheaper. Overall, this study shows that AI can be a helpful tool for labeling images, which could save time and money. |
