Improving Concept Alignment in Vision-Language Concept Bottleneck Models

• URL: http://arxiv.org/abs/2405.01825v1
• Date: Fri, 3 May 2024 03:02:00 GMT
• Title: Improving Concept Alignment in Vision-Language Concept Bottleneck Models
• Authors: Nithish Muthuchamy Selvaraj, Xiaobao Guo, Bingquan Shen, Adams Wai-Kin Kong, Alex Kot,
• Abstract summary: Concept Bottleneck Models (CBM) map the input image to a high-level human-understandable concept space and then make class predictions based on these concepts. Recent approaches automate the construction of CBM by prompting Large Language Models (LLM) to generate text concepts and then use Vision Language Models (VLM) to obtain concept scores to train a CBM. In this work, we take a closer inspection on the faithfulness of VLM concept scores for such expert-defined concepts in domains like fine-grain bird species classification and animal classification.
• Score: 8.60196823664609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept Bottleneck Models (CBM) map the input image to a high-level human-understandable concept space and then make class predictions based on these concepts. Recent approaches automate the construction of CBM by prompting Large Language Models (LLM) to generate text concepts and then use Vision Language Models (VLM) to obtain concept scores to train a CBM. However, it is desired to build CBMs with concepts defined by human experts instead of LLM generated concepts to make them more trustworthy. In this work, we take a closer inspection on the faithfulness of VLM concept scores for such expert-defined concepts in domains like fine-grain bird species classification and animal classification. Our investigations reveal that frozen VLMs, like CLIP, struggle to correctly associate a concept to the corresponding visual input despite achieving a high classification performance. To address this, we propose a novel Contrastive Semi-Supervised (CSS) learning method which uses a few labeled concept examples to improve concept alignment (activate truthful visual concepts) in CLIP model. Extensive experiments on three benchmark datasets show that our approach substantially increases the concept accuracy and classification accuracy, yet requires only a fraction of the human-annotated concept labels. To further improve the classification performance, we also introduce a new class-level intervention procedure for fine-grain classification problems that identifies the confounding classes and intervenes their concept space to reduce errors.

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