  Reconstructing seeing images from fMRI recordings is an absorbing research
area in neuroscience and provides a potential brain-reading technology. The
challenge lies in that visual encoding in brain is highly complex and not fully
revealed. Inspired by the theory that visual features are hierarchically
represented in cortex, we propose to break the complex visual signals into
multi-level components and decode each component separately. Specifically, we
decode shape and semantic representations from the lower and higher visual
cortex respectively, and merge the shape and semantic information to images by
a generative adversarial network (Shape-Semantic GAN). This 'divide and
conquer' strategy captures visual information more accurately. Experiments
demonstrate that Shape-Semantic GAN improves the reconstruction similarity and
image quality, and achieves the state-of-the-art image reconstruction
performance.
