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most citedTraining Strategies for Improved Lip-reading

59 citations · 246 across the 25 of their papers we have counts for

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cs.CV2021

Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups

Rafael Poyiadzi, Jie Shen, Stavros Petridis +2

Apparent emotional facial expression recognition has attracted a lot of research attention recently. However, the majority of approaches ignore age differences and train a generic…

cs.CV20214 cited

End-to-end Audio-visual Speech Recognition with Conformers

Pingchuan Ma, Stavros Petridis, Maja Pantic

In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In partic…

cs.CV2021

RoI Tanh-polar Transformer Network for Face Parsing in the Wild

Yiming Lin, Jie Shen, Yujiang Wang +1

Face parsing aims to predict pixel-wise labels for facial components of a target face in an image. Existing approaches usually crop the target face from the input image with respec…

cs.CV2020

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection

Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis +1

Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation m…

cs.CV2020

Enhancing Facial Data Diversity with Style-based Face Aging

Markos Georgopoulos, James Oldfield, Mihalis A. Nicolaou +2

A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as…

cs.CV2020

Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study

Markos Georgopoulos, Yannis Panagakis, Maja Pantic

Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias…