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20162026
most citedSynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision

3 citations · 5 across the 10 of their papers we have counts for

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cs.CV2024

MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization

Adriana Fernandez-Lopez, Honglie Chen, Pingchuan Ma +5

Pre-trained models have been a foundational approach in speech recognition, albeit with associated additional costs. In this study, we propose a regularization technique that facil…

cs.CV20242 cited

EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars

Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas +3

Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but high…

cs.CV2024

BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition

Alexandros Haliassos, Andreas Zinonos, Rodrigo Mira +2

Self-supervision has recently shown great promise for learning visual and auditory speech representations from unlabelled data. In this work, we propose BRAVEn, an extension to the…

cs.CV20233 cited

SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision

Xubo Liu, Egor Lakomkin, Konstantinos Vougioukas +9

Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video…

cs.CV2016

Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation

Adria Ruiz, Ognjen Rudovic, Xavier Binefa +1

In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression). Moreover,…