  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.
