2 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Dual Distillation for Few-Shot Anomaly Detection
Le Dong, Qinzhong Tan, Chunlei Li +5
Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. Wh…
LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction
Ao Li, Chen Chen, Zhenyu Wang +3
Exposure correction is essential for enhancing image quality under challenging lighting conditions. While supervised learning has achieved significant progress in this area, it rel…
High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation
Le Dong, Jinghao Bian, Jingyang Hou +5
Medical image analysis faces significant challenges in data sharing due to privacy regulations and complex institutional protocols. Dataset distillation offers a solution to addres…
S4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation
Jie Feng, Tianshu Zhang, Junpeng Zhang +4
Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data t…
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection
Jie Feng, Xiaojian Zhong, Di Li +3
Band selection plays a crucial role in hyperspectral image classification by removing redundant and noisy bands and retaining discriminative ones. However, most existing deep learn…
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
Jie Feng, Hao Huang, Junpeng Zhang +3
Mainstreamed weakly supervised road extractors rely on highly confident pseudo-labels propagated from scribbles, and their performance often degrades gradually as the image scenes…