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

Aligning a user query and video clips in cross-modal latent space and that
with semantic concepts are two mainstream approaches for ad-hoc video search
(AVS). However, the effectiveness of existing approaches is bottlenecked by the
small sizes of available video-text datasets and the low quality of concept
banks, which results in the failures of unseen queries and the
out-of-vocabulary problem. This paper addresses these two problems by
constructing a new dataset and developing a multi-word concept bank.
Specifically, capitalizing on a generative model, we construct a new dataset
consisting of 7 million generated text and video pairs for pre-training. To
tackle the out-of-vocabulary problem, we develop a multi-word concept bank
based on syntax analysis to enhance the capability of a state-of-the-art
interpretable AVS method in modeling relationships between query words. We also
study the impact of current advanced features on the method. Experimental
results show that the integration of the above-proposed elements doubles the
R@1 performance of the AVS method on the MSRVTT dataset and improves the xinfAP
on the TRECVid AVS query sets for 2016-2023 (eight years) by a margin from 2%
to 77%, with an average about 20%.

通过构建新数据集和发展多词概念库，本文解决了现有方法在出现未见查询和词汇量问题上的瓶颈，实验结果显示以上所述元素的整合将 AVS 方法在 MSRVTT 数据集上的 R@1 性能翻倍，并将在 2016-2023 年（八年）TRECVid AVS 查询集的 xinfAP 增加了 2% 到 77%，平均约为 20%。