Vision-and-language (V\&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the interplay between the two modalities. One crucial aspect of visual reasoning is spatial understanding, which involves understanding relative locations of objects, i.e.\ implicitly learning the geometry of the scene. In this work, we evaluate the faithfulness of V\&L models to such geometric understanding, by formulating the prediction of pair-wise relative locations of objects as a classification as well as a regression task. Our findings suggest that state-of-the-art transformer-based V\&L models lack sufficient abilities to excel at this task. Motivated by this, we design two objectives as proxies for 3D spatial reasoning (SR) -- object centroid estimation, and relative position estimation, and train V\&L with weak supervision from off-the-shelf depth estimators. This leads to considerable improvements in accuracy for the "GQA" visual question answering challenge (in fully supervised, few-shot, and O.O.D settings) as well as improvements in relative spatial reasoning. Code and data will be released \href{https://github.com/pratyay-banerjee/weak_sup_vqa}{here}.

这篇研究报告探讨了视觉和语言推理需要对视觉概念、语义和语言基础以及两种模式之间的相互作用进行感知，并评估了现有的 Vision-and-language 模型对于空间理解的忠实度。研究者提出了两个目标，利用现成的深度估计器，设计了关于三维空间推理的代理任务来训练 Vision-and-language 模型，使得在视觉问答挑战中取得了显著的表现改善。

视觉问答的弱监督相对空间推理