9 citations · 25 across the 16 of their papers we have counts for
5 papers · 1 filter
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +4
The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learnin…
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
Yun Li, Zhe Liu, Lina Yao +3
With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in E…
Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis
Yun Li, Zhe Liu, Lina Yao +2
EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are si…
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
Alvaro Gomariz, Huanxiang Lu, Yun Yvonna Li +7
Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been succ…
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning
Yun Li, Zhe Liu, Xiaojun Chang +2
Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential…