activity
20192023
most citedSelf-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

128 citations · 168 across the 9 of their papers we have counts for

collaborators

9 papers

cs.MM2023

Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5

Su Zhang, Ziyuan Zhao, Cuntai Guan

We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3,…

eess.IV2023★ 1 cited

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo +2

Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentati…

cs.CV2023

MetaGrad: Adaptive Gradient Quantization with Hypernetworks

Kaixin Xu, Alina Hui Xiu Lee, Ziyuan Zhao +3

A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference. However,…

cs.CV2022★ 2 cited

DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection

Ziyuan Zhao, Mingxi Xu, Peisheng Qian +2

Deep learning has achieved notable success in 3D object detection with the advent of large-scale point cloud datasets. However, severe performance degradation in the past trained c…

eess.IV2022★ 10 cited

ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation

Ziyuan Zhao, Andong Zhu, Zeng Zeng +2

While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especial…

eess.IV2022★ 24 cited

MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation

Ziyuan Zhao, Jinxuan Hu, Zeng Zeng +4

With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in cli…