  Visual reasoning is a special visual question answering problem that is
multi-step and compositional by nature, and also requires intensive text-vision
interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end
visual reasoning model. CMM includes a multi-step comprehension process for
both question and image. In each step, we use a Feature-wise Linear Modulation
(FiLM) technique to enable textual/visual pipeline to mutually control each
other. Experiments show that CMM significantly outperforms most related models,
and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR,
collected from both synthetic and natural languages. Ablation studies confirm
that both our multistep framework and our visual-guided language modulation are
critical to the task. Our code is available at
https://github.com/FlamingHorizon/CMM-VR.
