5 papers
Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations
Yang Yuxiang, Zeng Xinyi, Zeng Pinxian +4
Multi-source Domain Adaptation (MDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Nevertheless, traditional methods primarily focu…
SIT-FER: Integration of Semantic-, Instance-, Text-level Information for Semi-supervised Facial Expression Recognition
Sixian Ding, Xu Jiang, Zhongjing Du +3
Semi-supervised deep facial expression recognition (SS-DFER) has gained increasingly research interest due to the difficulty in accessing sufficient labeled data in practical setti…
BTMuda: A Bi-level Multi-source unsupervised domain adaptation framework for breast cancer diagnosis
Yuxiang Yang, Xinyi Zeng, Pinxian Zeng +4
Deep learning has revolutionized the early detection of breast cancer, resulting in a significant decrease in mortality rates. However, difficulties in obtaining annotations and hu…
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
Yuxiang Yang, Lu Wen, Xinyi Zeng +4
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single lab…
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
Jiaqi Cui, Xinyi Zeng, Pinxian Zeng +4
Radiation hazards associated with standard-dose positron emission tomography (SPET) images remain a concern, whereas the quality of low-dose PET (LPET) images fails to meet clinica…