13 citations · 46 across the 17 of their papers we have counts for
17 papers
Reversible Lifelong Model Editing via Semantic Routing-Based LoRA
Haihua Luo, Xuming Ran, Tommi Kärkkäinen +4
The dynamic evolution of real-world necessitates model editing within Large Language Models. While existing methods explore modular isolation or parameter-efficient strategies, the…
A Simple Efficiency Incremental Learning Framework via Vision-Language Model with Nonlinear Multi-Adapters
Haihua Luo, Xuming Ran, Jiangrong Shen +4
Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language mod…
Key-Value Pair-Free Continual Learner via Task-Specific Prompt-Prototype
Haihua Luo, Xuming Ran, Zhengji Li +6
Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this dom…
Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
Jiangrong Shen, Yulin Xie, Qi Xu +3
Multimodal spiking neural networks (SNNs) hold significant potential for energy-efficient sensory processing but face critical challenges in modality imbalance and temporal misalig…
Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Qi Xu, Junyang Zhu, Dongdong Zhou +4
Deep neural networks (DNNs) excel in computer vision tasks, especially, few-shot learning (FSL), which is increasingly important for generalizing from limited examples. However, DN…
Efficient ANN-SNN Conversion with Error Compensation Learning
Chang Liu, Jiangrong Shen, Xuming Ran +4
Artificial neural networks (ANNs) have demonstrated outstanding performance in numerous tasks, but deployment in resource-constrained environments remains a challenge due to their…
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
Qi Xu, Jie Deng, Jiangrong Shen +3
Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representati…
Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency
Jiangrong Shen, Qi Xu, Gang Pan +1
The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its…
ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning
Wenyao Ni, Jiangrong Shen, Qi Xu +1
Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental…
Context Gating in Spiking Neural Networks: Achieving Lifelong Learning through Integration of Local and Global Plasticity
Jiangrong Shen, Wenyao Ni, Qi Xu +2
Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spikin…
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
Yaxin Li, Qi Xu, Jiangrong Shen +3
The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models du…
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Qi Xu, Yuyuan Gao, Jiangrong Shen +4
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from…
Neuromorphic Auditory Perception by Neural Spiketrum
Huajin Tang, Pengjie Gu, Jayawan Wijekoon +4
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intellige…
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
Jiangrong Shen, Qi Xu, Jian K. Liu +3
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Qi Xu, Yaxin Li, Xuanye Fang +4
Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current s…
LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks
Di Hong, Jiangrong Shen, Yu Qi +1
Spiking Neural Networks (SNNs) are biologically realistic and practically promising in low-power computation because of their event-driven mechanism. Usually, the training of SNNs…
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Qi Xu, Yaxin Li, Jiangrong Shen +3
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, clo…