9 citations · 41 across the 7 of their papers we have counts for
11 papers
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Zhen Zhao, Lihe Yang, Sifan Long +3
Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly co…
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation
Zhen Zhao, Sifan Long, Jimin Pi +2
Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data eq…
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Shengqi Huang, Wanqi Yang, Lei Wang +2
This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the…
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images
Pin Tang, Chen Zu, Mei Hong +7
Precise and accurate segmentation of the most common head-and-neck tumor, nasopharyngeal carcinoma (NPC), in MRI sheds light on treatment and regulatory decisions making. However,…
Class Distribution Alignment for Adversarial Domain Adaptation
Wanqi Yang, Tong Ling, Chengmei Yang +4
Most existing unsupervised domain adaptation methods mainly focused on aligning the marginal distributions of samples between the source and target domains. This setting does not s…
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
Saimunur Rahman, Lei Wang, Changming Sun +1
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 c…