activity
20182022
most citedFacial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets

16 citations · 17 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2022

EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

Tien-Phat Nguyen, Trong-Thang Pham, Tri Nguyen +7

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Laps…

cs.CV202016 cited

Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets

In Seop Na, Chung Tran, Dung Nguyen +1

Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due t…

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.CV2018

Learning to Attend Relevant Regions in Videos from Eye Fixations

Thanh T. Nguyen, Dung Nguyen

Attentively important regions in video frames account for a majority part of the semantics in each frame. This information is helpful in many applications not only for entertainmen…