18 citations · 23 across the 4 of their papers we have counts for
8 papers
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Kai Qiao, Chi Zhang, Jian Chen +3
On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to present…
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai Qiao, Jian Chen, Linyuan Wang +3
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is di…
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Kai Qiao, Chi Zhang, Jian Chen +3
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encod…
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Kai Qiao, Jian Chen, Linyuan Wang +4
Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.…
A visual encoding model based on deep neural networks and transfer learning
Chi Zhang, Kai Qiao, Linyuan Wang +4
Background: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve hi…
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Chi Zhang, Xiaohan Duan, Linyuan Wang +5
Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still…