We study the problem of concept induction in visual reasoning, i.e.,
identifying concepts and their hierarchical relationships from question-answer
pairs associated with images; and achieve an interpretable model via working on
the induced symbolic concept space. To this end, we first design a new
framework named object-centric compositional attention model (OCCAM) to perform
the visual reasoning task with object-level visual features. Then, we come up
with a method to induce concepts of objects and relations using clues from the
attention patterns between objects' visual features and question words.
Finally, we achieve a higher level of interpretability by imposing OCCAM on the
objects represented in the induced symbolic concept space. Our model design
makes this an easy adaption via first predicting the concepts of objects and
relations and then projecting the predicted concepts back to the visual feature
space so the compositional reasoning module can process normally. Experiments
on the CLEVR and GQA datasets demonstrate: 1) our OCCAM achieves a new state of
the art without human-annotated functional programs; 2) our induced concepts
are both accurate and sufficient as OCCAM achieves an on-par performance on
objects represented either in visual features or in the induced symbolic
concept space.

该研究旨在通过目标 - 中心的组合式注意力模型（OCCAM）和符号概念空间，从与图像相关的问题 - 答案对中识别概念和它们的分层关系，并实现一个可解释的模型。该模型通过设计新的 OCCAM 框架执行视觉推理任务，并提出一种方法，从对象的视觉特征和问题词之间的注意力模式中使用线索，诱导对象和关系的概念。结果表明，OCCAM 在无需人类注释的功能程序的情况下取得了最新的技术水平，并且所诱导的概念精确而充足，在视觉特征或在引入的符号概念空间中表示的对象上都能够取得相当的性能。