Search Results for author: Qingqun Kong

Found 8 papers, 1 papers with code

Do We Need Binary Features for 3D Reconstruction?

no code implementations14 Feb 2016 Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua

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 descriptors.

3D Reconstruction

Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation

no code implementations6 Jul 2022 Yang Li, Xiang He, Yiting Dong, Qingqun Kong, Yi Zeng

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.

Bayesian Optimization object-detection +1

BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation

no code implementations18 Jul 2022 Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi

These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.

Decision Making

An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain

1 code implementation23 Mar 2023 Xiang He, Dongcheng Zhao, Yang Li, Guobin Shen, Qingqun Kong, Yi Zeng

In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data.

Transfer Learning

MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks

no code implementations23 Mar 2023 Xiang He, Yang Li, Dongcheng Zhao, Qingqun Kong, Yi Zeng

The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information.

Sentiment Analysis Sentiment Classification +2

Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks

no code implementations18 Sep 2023 Bing Han, Feifei Zhao, Wenxuan Pan, Zhaoya Zhao, Xianqi Li, Qingqun Kong, Yi Zeng

In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks.

Continual Learning

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