Concept-based models perform prediction using a set of concepts that are
interpretable to stakeholders. However, such models often involve a fixed,
large number of concepts, which may place a substantial cognitive load on
stakeholders. We propose Selective COncept Models (SCOMs) which make
predictions using only a subset of concepts and can be customised by
stakeholders at test-time according to their preferences. We show that SCOMs
only require a fraction of the total concepts to achieve optimal accuracy on
multiple real-world datasets. Further, we collect and release a new dataset,
CUB-Sel, consisting of human concept set selections for 900 bird images from
the popular CUB dataset. Using CUB-Sel, we show that humans have unique
individual preferences for the choice of concepts they prefer to reason about,
and struggle to identify the most theoretically informative concepts. The
customisation and concept selection provided by SCOM improves the efficiency of
interpretation and intervention for stakeholders.

SCOMs 是一种选择性概念模型，可以由利益相关者在测试时根据其喜好自定义，使用较少的概念即可在多个真实世界数据集上实现最佳准确性，并可显著提高利益相关者的解释和干预效率。

选择性概念模型：允许利益相关者在测试时进行自定义

Selective Concept Models: Permitting Stakeholder Customisation at  Test-Time

Visual storytelling is a task of generating relevant and interesting stories
for given image sequences. In this work we aim at increasing the diversity of
the generated stories while preserving the informative content from the images.
We propose to foster the diversity and informativeness of a generated story by
using a concept selection module that suggests a set of concept candidates.
Then, we utilize a large scale pre-trained model to convert concepts and images
into full stories. To enrich the candidate concepts, a commonsense knowledge
graph is created for each image sequence from which the concept candidates are
proposed. To obtain appropriate concepts from the graph, we propose two novel
modules that consider the correlation among candidate concepts and the
image-concept correlation. Extensive automatic and human evaluation results
demonstrate that our model can produce reasonable concepts. This enables our
model to outperform the previous models by a large margin on the diversity and
informativeness of the story, while retaining the relevance of the story to the
image sequence.

本文提出了一种基于概念选择模块和常识知识图谱的图像序列可视化叙事模型，以提高生成的故事的多样性和信息量，实现了很好的效果。