Concept Bottleneck Models (CBMs) map the black-box visual representations
extracted by deep neural networks onto a set of interpretable concepts and use
the concepts to make predictions, enhancing the transparency of the
decision-making process. Multimodal pre-trained models can match visual
representations with textual concept embeddings, allowing for obtaining the
interpretable concept bottleneck without the expertise concept annotations.
Recent research has focused on the concept bank establishment and the
high-quality concept selection. However, it is challenging to construct a
comprehensive concept bank through humans or large language models, which
severely limits the performance of CBMs. In this work, we propose the
Incremental Residual Concept Bottleneck Model (Res-CBM) to address the
challenge of concept completeness. Specifically, the residual concept
bottleneck model employs a set of optimizable vectors to complete missing
concepts, then the incremental concept discovery module converts the
complemented vectors with unclear meanings into potential concepts in the
candidate concept bank. Our approach can be applied to any user-defined concept
bank, as a post-hoc processing method to enhance the performance of any CBMs.
Furthermore, to measure the descriptive efficiency of CBMs, the Concept
Utilization Efficiency (CUE) metric is proposed. Experiments show that the
Res-CBM outperforms the current state-of-the-art methods in terms of both
accuracy and efficiency and achieves comparable performance to black-box models
across multiple datasets.

我们提出了递增残差概念瓶颈模型（Res-CBM），以解决概念完整性的挑战。通过优化向量来完成缺失的概念，并将不明确含义的补充向量转化为候选概念库中的潜在概念，以增强任何 CBM 的性能。实验结果表明，Res-CBM 在准确性和效率方面优于当前最先进的方法，并在多个数据集上实现了与黑盒模型相当的性能。

增量残差概念瓶颈模型

Incremental Residual Concept Bottleneck Models

Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural
networks that first predict labels for human-interpretable concepts relevant to
the prediction task, and then predict the final label based on the concept
label predictions.We extend CBMs to interactive prediction settings where the
model can query a human collaborator for the label to some concepts. We develop
an interaction policy that, at prediction time, chooses which concepts to
request a label for so as to maximally improve the final prediction. We
demonstrate thata simple policy combining concept prediction uncertainty and
influence of the concept on the final prediction achieves strong performance
and outperforms a static approach proposed in Koh et al. (2020) as well as
active feature acquisition methods proposed in the literature. We show that the
interactiveCBM can achieve accuracy gains of 5-10% with only 5 interactions
over competitive baselines on the Caltech-UCSDBirds, CheXpert and OAI datasets.

该研究提出一种交互式的 CBMs 模型，通过在预测过程中询问人类协作者对于某些概念的标签来提升最终预测准确率，并通过性能比较证明该模型相较于其他方法更为优越。