48 citations · 69 across the 18 of their papers we have counts for
6 papers · 1 filter
FocusVTC: Efficient and High-Performance Visual Text Compression with Adaptive Resolution
FangZhi Zhong, Xuerui Qiu, Yuqi Pan +4
Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by rendering text as images, but…
Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation
Zhenxin Lei, Man Yao, Jiakui Hu +4
Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex archit…
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
Xuerui Qiu, Man Yao, Jieyuan Zhang +5
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be w…
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
Man Yao, Xuerui Qiu, Tianxiang Hu +7
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major c…
Scalable Autoregressive Image Generation with Mamba
Haopeng Li, Jinyue Yang, Kexin Wang +4
We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performan…
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
Keming Wu, Man Yao, Yuhong Chou +4
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poi…