In this work, we present a computing platform named digital twin brain (...
Building robust, interpretable, and secure artificial intelligence syste...
Inspired by the highly irregular spiking activity of cortical neurons,
s...
Semantic neural decoding aims to elucidate the cognitive processes of th...
Score-based generative models (SGMs) have recently emerged as a promisin...
In this work, we propose a novel complementary learning approach to enha...
Segmenting the fine structure of the mouse brain on magnetic resonance (...
The human brain can effortlessly recognize and localize objects, whereas...
Visual representation learning is the key of solving various vision prob...
Score-based generative models (SGMs) have recently emerged as a promisin...
Vision transformers (ViTs) have pushed the state-of-the-art for various
...
Score-based generative models (SGMs) have recently emerged as a promisin...
Clustering is an essential task to unsupervised learning. It tries to
au...
Low-cost monocular 3D object detection plays a fundamental role in auton...
Monocular 3D object detection is a critical yet challenging task for
aut...
The objective of this paper is to learn context- and depth-aware feature...
Gait is a unique biometric feature that can be recognized at a distance;...
Most recent semantic segmentation methods adopt a fully-convolutional ne...
Object detection from 3D point clouds remains a challenging task, though...
We propose a novel fast and robust 3D point clouds segmentation framewor...
The relationship between the intelligence and brain morphology is warmly...
As a unique biometric feature that can be recognized at a distance, gait...
Inspired by the recent neuroscience studies on the left-right asymmetry ...