  Recently, visual encoding based on functional magnetic resonance imaging
(fMRI) have realized many achievements with the rapid development of deep
network computation. Visual encoding model is aimed at predicting brain
activity in response to presented image stimuli. Currently, visual encoding is
accomplished mainly by firstly extracting image features through convolutional
neural network (CNN) model pre-trained on computer vision task, and secondly
training a linear regression model to map specific layer of CNN features to
each voxel, namely voxel-wise encoding. However, the two-step manner model,
essentially, is hard to determine which kind of well features are well linearly
matched for beforehand unknown fMRI data with little understanding of human
visual representation. Analogizing computer vision mostly related human vision,
we proposed the end-to-end convolution regression model (ETECRM) in the region
of interest (ROI)-wise manner to accomplish effective and efficient visual
encoding. The end-to-end manner was introduced to make the model automatically
learn better matching features to improve encoding performance. The ROI-wise
manner was used to improve the encoding efficiency for many voxels. In
addition, we designed the selective optimization including self-adapting weight
learning and weighted correlation loss, noise regularization to avoid
interfering of ineffective voxels in ROI-wise encoding. Experiment demonstrated
that the proposed model obtained better predicting accuracy than the two-step
manner of encoding models. Comparative analysis implied that end-to-end manner
and large volume of fMRI data may drive the future development of visual
encoding.
