86 citations · 173 across the 5 of their papers we have counts for
5 papers
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Xu Yuan, Zheng Zhang, Xunguang Wang +1
Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized th…
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
Canran Li, Dongnan Liu, Haoran Li +4
Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models' adaptation ability in general computer vision. However, different from the natural im…
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
Bingzhi Chen, Yishu Liu, Zheng Zhang +2
Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years, convolutional encoder-decoder s…
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation
Ailiang Lin, Bingzhi Chen, Jiayu Xu +2
Automatic medical image segmentation has made great progress benefit from the development of deep learning. However, most existing methods are based on convolutional neural network…
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
Xunguang Wang, Zheng Zhang, Baoyuan Wu +2
Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep…