Summary of Vlg-cbm: Training Concept Bottleneck Models with Vision-language Guidance, by Divyansh Srivastava et al.
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
by Divyansh Srivastava, Ge Yan, Tsui-Wei Weng
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel framework called Vision-Language-Guided Concept Bottleneck Model (VLG-CBM) to improve the interpretability and performance of Concept Bottleneck Models (CBMs). CBMs introduce an intermediate Concept Bottleneck Layer (CBL) that encodes human-understandable concepts to explain models’ decision-making. Existing approaches have limitations, such as mismatched concept predictions and unintended information encoded in concept values. The VLG-CBM framework leverages open-domain grounded object detectors for visually grounded concept annotation, enhancing faithfulness and performance. A new metric, Number of Effective Concepts (NEC), is introduced to control information leakage and provide better interpretability. Extensive evaluations on five standard benchmarks demonstrate that VLG-CBM outperforms existing methods by up to 51.09% in accuracy at NEC=5 and by up to 29.78% in average accuracy, while preserving faithfulness and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making AI models more understandable and accurate. Right now, there’s a problem with how these models make decisions because the concepts they use don’t always match what we see. The authors propose a new way to improve this by using object detectors that understand images to help the model learn better concepts. This new approach also helps control how much information is leaked from the concept values, making it more reliable. They tested their method on several datasets and found that it outperformed other methods in both accuracy and faithfulness. |
