  Reasoning about visual relationships is central to how humans interpret the
visual world. This task remains challenging for current deep learning
algorithms since it requires addressing three key technical problems jointly:
1) identifying object entities and their properties, 2) inferring semantic
relations between pairs of entities, and 3) generalizing to novel
object-relation combinations, i.e., systematic generalization. In this work, we
use vision transformers (ViTs) as our base model for visual reasoning and make
better use of concepts defined as object entities and their relations to
improve the reasoning ability of ViTs. Specifically, we introduce a novel
concept-feature dictionary to allow flexible image feature retrieval at
training time with concept keys. This dictionary enables two new concept-guided
auxiliary tasks: 1) a global task for promoting relational reasoning, and 2) a
local task for facilitating semantic object-centric correspondence learning. To
examine the systematic generalization of visual reasoning models, we introduce
systematic splits for the standard HICO and GQA benchmarks. We show the
resulting model, Concept-guided Vision Transformer (or RelViT for short)
significantly outperforms prior approaches on HICO and GQA by 16% and 13% in
the original split, and by 43% and 18% in the systematic split. Our ablation
analyses also reveal our model's compatibility with multiple ViT variants and
robustness to hyper-parameters.
