Connectionists: [jobs] PhD opportunity - Spatio-temporal augmentation models for motion pattern learning - AI_PhD at Lille, France

[Ioan Marius BILASCO]

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is 
looking to recruit a PhD student starting on *October 1st 2022* on the 
following subject : *Spatio-temporal data augmentation models for motion 
pattern learning using deep learning: applications to facial analysis in 
the wild*

The FOX research group is part of the CRIStAL laboratory (University of 
Lille, CNRS), located in Lille, France. We focus on video analysis for 
human behavior understanding. Specifically, we develop spatio-temporal 
models of motions for tasks such as abnormal event detection, emotion 
recognition, and face alignment. Our work is published in major journals 
(Pattern Recognition, IEEE Trans. on Affective Computing) and 
conferences (WACV, IJCNN).

This PHD thesis will be funded in the framework of the 
*AI_PhD at Lille*program.
http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/

The candidate will be funded for 3 years; he/she is expected to defend 
his/her thesis and graduate by the end of the contract. The monthly net 
salary is around *1800*€, including benefits (health insurance, 
retirement fund, and paid vacations).

The position is located in *Lille, France*. With over 110 000 students, 
the metropolitan area of Lille is one France's top education student 
cities. The European Doctoral College Lille Nord-Pas de Calais is 
headquartered in Lille Metropole and includes 3,000 PhD Doctorate 
students supported by university research laboratories. Lille has a 
convenient location in the European high-speed rail network. It lies on 
the Eurostar line to London (1:20 hour journey). The French TGV network 
also puts it only 1 hour from Paris, 35 mn from Brussels, and a short 
trips to other major centres in France such as Paris, Marseille and Lyon.


*Abstract*: Facial expression analysis is a well-studied field when 
dealing with segmented and constrained data captured in lab conditions. 
However, many challenges must still be addressed for building 
in-the-wild solutions that account for various motion intensities, 
strong head movements during expressions, the spotting of the 
subsequence containing the expression, partially occluded faces, etc. In 
recent years, learned features based on deep learning architectures were 
proposed in order to deal with these challenges. Deep learning is 
characterized by neural architectures that depend on a huge number of 
parameters. The convergence of these neural networks and the estimation 
of optimal parameters require large amounts of training data, especially 
when dealing with spatio-temporal data, particulary adequate for facial 
expression recognition. The quantity, but also the quality, of the data 
and its capacity to reflect the addressed challenges are key elements 
for training properly the networks. Augmenting the data artificially in 
an intelligent and controlled way is an interesting solution. The 
augmentation techniques identified in the literature are mainly focused 
on image augmentation and consist of scaling, rotation, and flipping 
operations, or they make use of more complex adversarial training. These 
techniques can be applied at the frame level, but there is a need for 
sequence level augmentation in order to better control the augmentation 
process and ensure the absence of temporal artifacts that might bias the 
learning process. The generation of dynamic frontal facial expressions 
has already been addressed in the literature. The goal of this Ph.D. is 
to conceive new space-time augmentation methods for unconstrained facial 
analysis (involving head movements, occultations, etc.). Attention 
should be paid in assessing the quality standards related to facial 
expression requirements: stability over time, absence of facial 
artifacts, etc. More specifically, the Ph.D. candidate is expected to 
conceive augmentation architectures that address various challenges 
(motion intensities, head movements) while maintaining temporal 
stability and eliminating facial artifacts.

More details are available here : https://bit.ly/staugm_motion

Candidates must hold a Master degree in Computer Science, Statistics, 
Applied Mathematics or a related field. Experience in one or more of the 
following is a plus:
• image processing, computer vision;
• machine learning;
• research methodology (literature review, experimentation…).

Candidates should have the following skills:
• good proficiency in English, both spoken and written;
• scientific writing;
• programming (experience in C++ is a plus, but not mandatory).

We look forward to receiving your application/as soon as possible/
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