Developing a series of brain-inspired intelligence methods, with characteristics of good generalization, adaptability, robustness and interpretability.
Building an AI testing system in open environments, with database and testing protocols, supporting various AI tasks.
Founding an innovative research team with international influence.
Brain-inspired intelligence learning methods
Perception application with standards and strategies for comprehensive evaluation in open domain.
Differentiable Architecture Approximation (DATA) with an Ensemble Gumbel-Softmax (EGS) estimator.
Architecture parameters and network weights in the NAS model can be jointly optimized with the standard BP.
Exploiting convolutional neural network for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds.
A novel differential convolution search paradigm on point clouds, which works in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
It is the first time to highlight the generalized object recognition problem.
Propose a meta-leaning object recognition in unseen domains, combining gradient and meta-gradient in optimization.
Propose two benchmarks for open-set generalized object recognition evaluation.
Besides, we also use meta-learning to propose fast domain adaptation and discriminative image labeling way.
Prototypes representing class distribution
Feature space: desired to be compact within each class