17 citations · 30 across the 21 of their papers we have counts for
9 papers · 1 filter
Relative Difficulty Distillation for Semantic Segmentation
Dong Liang, Yue Sun, Yun Du +2
Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student net…
Transfer CLIP for Generalizable Image Denoising
Jun Cheng, Dong Liang, Shan Tan
Image denoising is a fundamental task in computer vision. While prevailing deep learning-based supervised and self-supervised methods have excelled in eliminating in-distribution n…
InvKA: Gait Recognition via Invertible Koopman Autoencoder
Fan Li, Dong Liang, Jing Lian +3
Most current gait recognition methods suffer from poor interpretability and high computational cost. To improve interpretability, we investigate gait features in the embedding spac…
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems
Huayu Wang, Chen Luo, Taofeng Xie +4
Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit ce…
Patched Line Segment Learning for Vector Road Mapping
Jiakun Xu, Bowen Xu, Gui-Song Xia +2
This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation f…
ALL-E: Aesthetics-guided Low-light Image Enhancement
Ling Li, Dong Liang, Yuanhang Gao +2
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods…