140 citations · 260 across the 14 of their papers we have counts for
14 papers
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
Yizhuo Lu, Changde Du, Qiongyi zhou +2
Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task. Especially, the achievement of precise and controllable image reconstruction bears g…
Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning
Xiaoyu Chen, Changde Du, Qiongyi Zhou +1
The human brain can easily focus on one speaker and suppress others in scenarios such as a cocktail party. Recently, researchers found that auditory attention can be decoded from t…
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
Yizhuo Lu, Changde Du, Dianpeng Wang +1
Reconstructing visual stimuli from measured functional magnetic resonance imaging (fMRI) has been a meaningful and challenging task. Previous studies have successfully achieved rec…
PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition
Ming Jin, Enwei Zhu, Changde Du +2
Emotion recognition is essential in the diagnosis and rehabilitation of various mental diseases. In the last decade, electroencephalogram (EEG)-based emotion recognition has been i…
Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
Kaicheng Fu, Changde Du, Shengpei Wang +1
Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is…
Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features
Changde Du, Kaicheng Fu, Jinpeng Li +1
Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligen…
Multimodal foundation models are better simulators of the human brain
Haoyu Lu, Qiongyi Zhou, Nanyi Fei +8
Multimodal learning, especially large-scale multimodal pre-training, has developed rapidly over the past few years and led to the greatest advances in artificial intelligence (AI).…
Going Deeper into Permutation-Sensitive Graph Neural Networks
Zhongyu Huang, Yingheng Wang, Chaozhuo Li +1
The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite c…
MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions
Penghua Zhai, Huaiwei Cong, Gangming Zhao +4
\emph{Objective and Impact Statement}. With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications.…
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition
Hao Chen, Ming Jin, Zhunan Li +3
As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress du…
GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images
Baolian Qi, Gangming Zhao, Xin Wei +4
Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such a…
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
Changde Du, Changying Du, Hao Wang +4
There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to m…
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
Changde Du, Changying Du, Jinpeng Li +3
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly…
Sharing deep generative representation for perceived image reconstruction from human brain activity
Changde Du, Changying Du, Huiguang He
Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in br…