1 citations · 1 across the 3 of their papers we have counts for
4 papers
LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs
Shiqi Ding, Xuanyu Liang, Xinyang Liu +4
Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select quantization scales. We present…
FFR: Forward-Forward Learning for Regression
Xinyang Liu, Xuanyu Liang, Shiqi Ding +4
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely loca…
Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach
Jiayang Li, Chengjie Jiang, Junjun Jiang +3
Image fusion aims to blend complementary information from multiple sensing modalities, yet existing approaches remain limited in robustness, adaptability, and controllability. Most…
MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training
Jiayang Li, Junjun Jiang, Pengwei Liang +2
In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on trainin…