3 citations · 5 across the 6 of their papers we have counts for
6 papers
BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding
Xuan-Bac Nguyen, Thanh-Dat Truong, Pawan Sinha +1
Memory decay makes it harder for the human brain to recognize visual objects and retain details. Consequently, recorded brain signals become weaker, uncertain, and contain poor vis…
COBRA: A Continual Learning Approach to Vision-Brain Understanding
Xuan-Bac Nguyen, Manuel Serna-Aguilera, Arabinda Kumar Choudhary +3
Vision-Brain Understanding (VBU) aims to extract visual information perceived by humans from brain activity recorded through functional Magnetic Resonance Imaging (fMRI). Despite n…
Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding
Hoang-Quan Nguyen, Xuan-Bac Nguyen, Hugh Churchill +4
Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usuall…
Configural processing as an optimized strategy for robust object recognition in neural networks
Hojin Jang, Pawan Sinha, Xavier Boix
Configural processing, the perception of spatial relationships among an object's components, is crucial for object recognition. However, the teleology and underlying neurocomputati…
BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning
Xuan-Bac Nguyen, Hojin Jang, Xin Li +3
The human brain is a highly efficient processing unit, and understanding how it works can inspire new algorithms and architectures in machine learning. In this work, we introduce a…
Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI
Xuan-Bac Nguyen, Xin Li, Pawan Sinha +2
Human perception plays a vital role in forming beliefs and understanding reality. A deeper understanding of brain functionality will lead to the development of novel deep neural ne…