Kup-Sze Choi — accepted AI-conference papers

2023

FedEEG: Federated EEG Decoding Via inter-Subject Structure Matching

Wenlong Hang, Jiaxing Li, Shuang Liang, Yuan Wu, Baiying Lei, Jing Qin +2

ICASSP 2023accepted

With sufficient centralized training data coming from multiple subjects, deep learning methods have achieved powerful EEG decoding performance. However, sending each individuals’ EEG data directly to a centralized server might cause privacy leakage. To overcome this issue, we present an inter-subjec…

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2021

Object Detection in Densely Packed Scenes via Semi-Supervised Learning with Dual Consistency

Chao Ye, Huaidong Zhang, Xuemiao Xu, Weiwei Cai, Jing Qin, Kup-Sze Choi

IJCAI 2021poster

Deep neural networks have been shown to be very powerful tools for object detection in various scenes. Their remarkable performance, however, heavily depends on the availability of a large number of high quality labeled data, which are time-consuming and costly to acquire for scenes with densely pac…

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