  When answering questions about an image, it not only needs knowing what --
understanding the fine-grained contents (e.g., objects, relationships) in the
image, but also telling why -- reasoning over grounding visual cues to derive
the answer for a question. Over the last few years, we have seen significant
progress on visual question answering. Though impressive as the accuracy grows,
it still lags behind to get knowing whether these models are undertaking
grounding visual reasoning or just leveraging spurious correlations in the
training data. Recently, a number of works have attempted to answer this
question from perspectives such as grounding and robustness. However, most of
them are either focusing on the language side or coarsely studying the
pixel-level attention maps. In this paper, by leveraging the step-wise object
grounding annotations provided in the GQA dataset, we first present a
systematical object-centric diagnosis of visual reasoning on grounding and
robustness, particularly on the vision side. According to the extensive
comparisons across different models, we find that even models with high
accuracy are not good at grounding objects precisely, nor robust to visual
content perturbations. In contrast, symbolic and modular models have a
relatively better grounding and robustness, though at the cost of accuracy. To
reconcile these different aspects, we further develop a diagnostic model,
namely Graph Reasoning Machine. Our model replaces purely symbolic visual
representation with probabilistic scene graph and then applies teacher-forcing
training for the visual reasoning module. The designed model improves the
performance on all three metrics over the vanilla neural-symbolic model while
inheriting the transparency. Further ablation studies suggest that this
improvement is mainly due to more accurate image understanding and proper
intermediate reasoning supervisions.
