28 citations · 35 across the 5 of their papers we have counts for
5 papers
A Multi-View Consistency Framework with Semi-Supervised Domain Adaptation
Yuting Hong, Li Dong, Xiaojie Qiu +4
Semi-Supervised Domain Adaptation (SSDA) leverages knowledge from a fully labeled source domain to classify data in a partially labeled target domain. Due to the limited number of…
Exploiting Minority Pseudo-Labels for Semi-Supervised Fine-grained Road Scene Understanding
Yuting Hong, Yongkang Wu, Hui Xiao +4
In fine-grained road scene understanding, semantic segmentation plays a crucial role in enabling vehicles to perceive and comprehend their surroundings. By assigning a specific cla…
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation
Hui Xiao, Yuting Hong, Li Dong +5
Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly…
Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification
Hao Xu, Hui Xiao, Huazheng Hao +3
Semi-supervised learning frameworks usually adopt mutual learning approaches with multiple submodels to learn from different perspectives. To avoid transferring erroneous pseudo la…
Semi-Supervised Semantic Segmentation with Cross Teacher Training
Hui Xiao, Li Dong, Kangkang Song +4
Convolutional neural networks can achieve remarkable performance in semantic segmentation tasks. However, such neural network approaches heavily rely on costly pixel-level annotati…