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.

本文利用视觉转换器 (ViTs) 作为我们视觉推理的基本模型，通过优化定义为物体实体及其关系概念，推动 ViTs 的推理能力，并介绍了一种新的概念特征字典，以促进全局关系推理和促进语义对象特定一一对应关系学习的局部任务。结果显示，我们的模型 Concept-guided Vision Transformer（或 RelViT）在 HICO 和 GQA 上的性能均优于先前的方法，并充分考虑了 ViT 变体和超参数的稳健性。