99 citations · 536 across the 34 of their papers we have counts for
8 papers · 1 filter
BI AVAN: Brain inspired Adversarial Visual Attention Network
Heng Huang, Lin Zhao, Xintao Hu +4
Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks. However, most of the visual attention st…
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
Lin Zhao, Haixing Dai, Zihao Wu +8
Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning…
Rectify ViT Shortcut Learning by Visual Saliency
Chong Ma, Lin Zhao, Yuzhong Chen +7
Shortcut learning is common but harmful to deep learning models, leading to degenerated feature representations and consequently jeopardizing the model's generalizability and inter…
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding
Lu Zhang, Xiaowei Yu, Yanjun Lyu +7
Effective representation of brain anatomical architecture is fundamental in understanding brain regularity and variability. Despite numerous efforts, it is still difficult to infer…
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
Chong Ma, Lin Zhao, Yuzhong Chen +15
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning the meaningful and useful representations, thus jeopardizing the gen…
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
Yuzhong Chen, Yu Du, Zhenxiang Xiao +8
Vision transformer (ViT) and its variants have achieved remarkable successes in various visual tasks. The key characteristic of these ViT models is to adopt different aggregation s…
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
Yuzhong Chen, Zhenxiang Xiao, Lin Zhao +10
Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gaine…
Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers
Xiaowei Yu, Lu Zhang, Lin Zhao +3
How to identify and characterize functional brain networks (BN) is fundamental to gain system-level insights into the mechanisms of brain organizational architecture. Current funct…