Corentin Kervadec

Logo

PhD Student @ INSA Lyon & Orange.

I work on Deep Learning applied to Vision and Language, with a special interest on Visual Reasoning and VQA.

View My GitHub Profile

Follow me on twitter

Send me an email

Google scholar

I am a PhD student at INSA Lyon financed by Orange Labs in the Imagine team (LIRIS) under the direction of Christian Wolf.

My thesis is also co-supervised by Grigory Antipov and Moez Baccouche from Orange Labs.

My work focuses on Deep Learning applied to Vision and Language, with a special interest on Visual Reasoning and Visual Question Answering (VQA).

News

Publications

Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?

blind-date


Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf
Arxiv, 2020  
PDF / arXiv / Code / Benchmark

We propose GQA-OOD, a new benchmark to evaluate VQA in out-of-distribution settings by reorganizing the GQA dataset, taylored for each sample (question group), targeting research in bias reduction in VQA.

Estimating semantic structure for the VQA answer space

blind-date


Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf
Arxiv, 2020  
PDF / arXiv

Semantic loss for VQA adding structure to the VQA answer space estimated from redundancy in annotations, questioning the classification approach to VQA.

Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks

blind-date


Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf
ECAI, 2020  
PDF / arXiv / video (soon) / bibtex

We introduce a weakly supervised word-object alignment inside BERT-like Vision-Language encoders, allowing to model fine-grained entity relations and improve visual reasoning capabilities.

The Many Variations of Emotion

blind-date


Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Frederic Jurie
FG, 2019  
PDF / bibtex

We present a novel approach for changing facial expression in images by the use of a continuous latent space of emotion.

CAKE: Compact and Accurate K-dimensional representation of Emotion

blind-date


Corentin Kervadec*, Valentin Vielzeuf*, Stéphane Pateux, Alexis Lechervy, Frederic Jurie
IAHFAR workshop (BMVC), 2018  
PDF / arXiv / bibtex

We propose CAKE, a 3-dimensional representation of emotion learned in a multi-domain fashion, achieving accurate emotion recognition on several public datasets

An occam's razor view on learning audiovisual emotion recognition with small training sets

Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Alexis Lechervy, Frederic Jurie
EmotiW challenge (ICMI), 2018  
PDF / bibtex

A light-weight and accurate deep neural model for audiovisual emotion recognition. We ranked 3th at the Emotion in the Wild 2018 challenge.