Search Results for author:
Found 9 papers, 7 papers with code
BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition
In this work, we propose BRAVEn, an extension to the recent RAVEn method, which learns speech representations entirely from raw audio-visual data.
SparseVSR: Lightweight and Noise Robust Visual Speech Recognition
We evaluate our 50% sparse model on 7 different visual noise types and achieve an overall absolute improvement of more than 2% WER compared to the dense equivalent.
Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets.
Ranked #1 on
Automatic Speech Recognition (ASR)
on LRS3-TED
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Learning Cross-lingual Visual Speech Representations
Cross-lingual self-supervised learning has been a growing research topic in the last few years.
Jointly Learning Visual and Auditory Speech Representations from Raw Data
We observe strong results in low- and high-resource labelled data settings when fine-tuning the visual and auditory encoders resulting from a single pre-training stage, in which the encoders are jointly trained.
Ranked #1 on
Speech Recognition
on LRS2
(using extra training data)
SVTS: Scalable Video-to-Speech Synthesis
Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio.
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression.
Ranked #2 on
DeepFake Detection
on FakeAVCeleb
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance.
Ranked #5 on
DeepFake Detection
on FakeAVCeleb
Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach
However, both TT and other Tensor Networks (TNs), such as Tensor Ring and Hierarchical Tucker, are sensitive to the ordering of their indices (and hence to the features).
