87 citations · 113 across the 11 of their papers we have counts for
12 papers
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
Xiang He, Dongcheng Zhao, Yang Li +3
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion rese…
Similarity-based context aware continual learning for spiking neural networks
Bing Han, Feifei Zhao, Yang Li +3
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environmen…
CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization
Xiang He, Xiangxi Liu, Yang Li +5
The audio-visual event localization task requires identifying concurrent visual and auditory events from unconstrained videos within a network model, locating them, and classifying…
Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference
Yang Li, Xiang He, Qingqun Kong +1
Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference. However, the direct training of deep spiking neural netw…
Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao +17
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument…
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
Bing Han, Feifei Zhao, Wenxuan Pan +4
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms…
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
Xiang He, Yang Li, Dongcheng Zhao +2
Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting we…
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
Xiang He, Dongcheng Zhao, Yang Li +3
Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less an…
Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation
Yang Li, Xiang He, Yiting Dong +2
Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware. As an…
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
Yi Zeng, Dongcheng Zhao, Feifei Zhao +16
Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological…
A Performance Evaluation of Local Features for Image Based 3D Reconstruction
Bin Fan, Qingqun Kong, Xinchao Wang +4
This paper performs a comprehensive and comparative evaluation of the state of the art local features for the task of image based 3D reconstruction. The evaluated local features co…
Do We Need Binary Features for 3D Reconstruction?
Bin Fan, Qingqun Kong, Wei Sui +5
Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptor…