11 citations · 21 across the 15 of their papers we have counts for
6 papers · 1 filter
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
Differential Coding for Training-Free ANN-to-SNN Conversion
Zihan Huang, Wei Fang, Tong Bu +6
Spiking Neural Networks (SNNs) exhibit significant potential due to their low energy consumption. Converting Artificial Neural Networks (ANNs) to SNNs is an efficient way to achiev…
Towards High-performance Spiking Transformers from ANN to SNN Conversion
Zihan Huang, Xinyu Shi, Zecheng Hao +4
Spiking neural networks (SNNs) show great potential due to their energy efficiency, fast processing capabilities, and robustness. There are two main approaches to constructing SNNs…
USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting
Kang Chen, Jiyuan Zhang, Zecheng Hao +3
Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task via Neural Radi…
UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction
Zeyu Wang, Zecheng Hao, Jingyu Lin +2
This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from ex…
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Zecheng Hao +5
The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention. It utilizes binary spike activations to…