  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.
