4 citations · 8 across the 5 of their papers we have counts for
6 papers
Test-time Fourier Style Calibration for Domain Generalization
Xingchen Zhao, Chang Liu, Anthony Sicilia +2
The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods…
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
Kai Li, Chang Liu, Handong Zhao +2
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled sample…
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
Zhuqing Jiang, Chang Liu, Ya'nan Wang +4
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography. Images capt…
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
Ya'nan Wang, Zhuqing Jiang, Chang Liu +3
It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. H…
HyperSTAR: Task-Aware Hyperparameters for Deep Networks
Gaurav Mittal, Chang Liu, Nikolaos Karianakis +3
While deep neural networks excel in solving visual recognition tasks, they require significant effort to find hyperparameters that make them work optimally. Hyperparameter Optimiza…
RGB-D Individual Segmentation
Wenqiang Xu, Yanjun Fu, Yuchen Luo +2
Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications. In this work, we are particularly interested in the se…