activity
20192021
most citedDSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

88 citations · 117 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV20212 cited

Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion

Su Zhang, Yi Ding, Ziquan Wei +1

We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an a…

cs.LG20214 cited

Convolutional Neural Network Interpretability with General Pattern Theory

Erico Tjoa, Guan Cuntai

Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefi…

cs.CV202188 cited

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

Ziyuan Zhao, Zeng Zeng, Kaixin Xu +2

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Thi…

cs.CV2020

Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network

Erico Tjoa, Guan Cuntai

The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intell…

eess.SP202010 cited

TSception: A Deep Learning Framework for Emotion Detection Using EEG

Yi Ding, Neethu Robinson, Qiuhao Zeng +4

In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional laye…

cs.LG2020

Federated Transfer Learning for EEG Signal Classification

Ce Ju, Dashan Gao, Ravikiran Mane +3

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lac…