6 papers
Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization
Qiugang Zhan, Anning Jiang, Ran Tao +4
Pre-trained vision models have found widespread application across diverse domains. Prompt tuning-based methods have emerged as a parameter-efficient paradigm for adapting pre-trai…
SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning
Ran Tao, Qiugang Zhan, Shantian Yang +3
Spiking Federated Learning (SFL) has been widely studied with the energy efficiency of Spiking Neural Networks (SNNs). However, existing SFL methods require model homogeneity and a…
Falcon: A Cross-Modal Evaluation Dataset for Comprehensive Safety Perception
Qi Xue, Minrui Jiang, Runjia Zhang +3
Existing methods for evaluating the harmfulness of content generated by large language models (LLMs) have been well studied. However, approaches tailored to multimodal large langua…
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation
Muquan Li, Dongyang Zhang, Tao He +3
Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data.…
ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling
Qiugang Zhan, Ran Tao, Xiurui Xie +4
In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the…
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning
Qiugang Zhan, Jinbo Cao, Xiurui Xie +3
Spiking federated learning is an emerging distributed learning paradigm that allows resource-constrained devices to train collaboratively at low power consumption without exchangin…