Summary of Are They the Same Picture? Adapting Concept Bottleneck Models For Human-ai Collaboration in Image Retrieval, by Vaibhav Balloli et al.
Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrieval
by Vaibhav Balloli, Sara Beery, Elizabeth Bondi-Kelly
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 model, CHAIR, improves upon traditional human-in-the-loop approaches by enabling humans to correct intermediate concepts in a deep learning-based image retrieval system. Building on the Concept Bottleneck Model (CBM), CHAIR allows for flexible levels of human intervention, accommodating varying levels of expertise and ultimately achieving better retrieval performance. The model is evaluated using various metrics and outperforms similar models without any external intervention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CHAIR is a new way to improve image retrieval by letting humans correct the AI’s mistakes. Usually, humans do tasks independently and then combine their results with the AI’s, but this can be slow and expensive. CHAIR lets humans correct ideas (concepts) in the middle of the process, making it faster and more accurate. It also works well with different levels of human expertise. The test shows that CHAIR does better than other models without help from people. |
Keywords
» Artificial intelligence » Deep learning
