5 papers
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…
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…
Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen +3
Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current co…
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…
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
Trong-Thuan Nguyen, Pha Nguyen, Xin Li +3
Video scene graph generation (VidSGG) has emerged as a transformative approach to capturing and interpreting the intricate relationships among objects and their temporal dynamics i…