4 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
IMU: Influence-guided Machine Unlearning
Xindi Fan, Jing Wu, Mingyi Zhou +3
Machine Unlearning (MU) aims to selectively erase the influence of specific data points from pretrained models. However, most existing MU methods rely on the retain set to preserve…
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond
Pengwei Liang, Junjun Jiang, Qing Ma +2
Image fusion is famous as an alternative solution to generate one high-quality image from multiple images in addition to image restoration from a single degraded image. The essence…
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…
Image Deblurring by Exploring In-depth Properties of Transformer
Pengwei Liang, Junjun Jiang, Xianming Liu +1
Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to impro…
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
Pengwei Liang, Junjun Jiang, Xianming Liu +1
The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kern…