activity
20182022
most citedMachine learning for the recognition of emotion in the speech of couples in psychotherapy using the Stanford Suppes Brain Lab Psychotherapy Dataset

8 citations · 11 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CV2022

PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors

Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen +1

In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on…

cs.CV2020

Meta Transfer Learning for Emotion Recognition

Dung Nguyen, Sridha Sridharan, Duc Thanh Nguyen +3

Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient annotated emotion datasets, p…

cs.CV20201 cited

Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

Dung Nguyen, Duc Thanh Nguyen, Rui Zeng +5

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a sign…

cs.CV2020

Joint Deep Cross-Domain Transfer Learning for Emotion Recognition

Dung Nguyen, Sridha Sridharan, Duc Thanh Nguyen +4

Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient tra…

cs.CV2020

SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information

Jing Yu Koh, Duc Thanh Nguyen, Quang-Trung Truong +2

Fully-automatic execution is the ultimate goal for many Computer Vision applications. However, this objective is not always realistic in tasks associated with high failure costs, s…

cs.CV2019

LCD: Learned Cross-Domain Descriptors for 2D-3D Matching

Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua +3

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network…