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23 février 2021

PhD Spatio-temporal augmentation models for motion pattern learning - AI_PhD@Lille, France


Catégorie : Doctorant


The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student 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).

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 :http://bit.ly/st_augm_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).


This PHD thesis will be funded in the framework of theAI_PhD@Lilleprogram.
http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/
http://www.isite-ulne.fr/wp-content/uploads/2021/02/The%CC%80ses-IA_Campagne2021_I_Site_GB.pdf

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 gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations). Additional financial support is expected in the framework of theAI_PhD@Lilleprogram.

The position is located inLille, 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.

We look forward to receiving your applicationas soon as possible, but no later than14.03.2021.

 

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