Deep Learning Engineer at Veesion

Job Description

The mission mainly consists in improving the accuracy of the algorithm we are developing on a proprietary video dataset. Tasks will include:

-Tracking down and implementing state-of-the-art video recognition deep learning architectures [1-2-3-4]
-Implementing a light optical flow based action recognition model [6]
-Designing intermediary descriptors [4-5]
-Designing an unsupervised approach to the problem [7]
-Mastering general data preprocessing techniques [8] and temporal data augmentation
-Implementing an efficient pruning for all neural networks used
-Improving the multithreading and multiprocessing training and inference pipelines
-Collaborating with the Integration team and the Product Development team
-Assisting the CTO in tech related appointments and collaboration with labs
-Explore new tasks related to the algorithm arising from tech watch

Requirements

– Proven expertise in Deep Learning, especially in computer vision (video related tasks are a plus)
– Interest in research and ability to extract useful information from scientific papers
– Solid grasp of at least one of the following deep learning frameworks : Pytorch, Tensorflow, CNTK, Caffe
– Understanding of convolution and famous related architectures (resnext, I3D, RPN, two-stream networks)
– Analytical mind, ability to take a step back and see the big picture
– Problem-solving aptitude
– Master / PhD in Deep Learning or relevant fields.

Bibliography

[1] Bolei Zhou, Alex Andonian, Antonio Torralba : Temporal Relational Reasoning in Videos arXiv:1711.08496

[2] Jiawei He, Mostafa S. Ibrahim, Zhiwei Deng, Greg Mori : Generic Tubelet Proposals for Action Localization arXiv:1705.10861

[3] Sijie Yan, Yuanjun Xiong, Dahua Lin : Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition arXiv:1801.07455

[4] Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu : Spatial Transformer Networks arXiv:1506.02025

[5] Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann : Deep Local Video Feature for Action Recognition arXiv:1701.07368

[6] Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox : FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks arXiv:1612.01925

[7] Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, Georg Langs : Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery arXiv:1703.05921

[8] https://github.com/TwentyBN/GulpIO

Office

Paris – Full time

Compensation

€40k – €80k

About Veesion

Founded by Polytechnique and HEC alumni, Veesion is incubated at Agoranov (96 bis Boulevard Raspail, 75006 Paris).

We are developing a gesture recognition technology in video content. The potential applications are diverse and our first focus is shoplifting detection in the retail industry.

We built a large video database thanks to partnerships with major retailers and we are in constant discussion with top-notch Computer Vision labs such as the Thoth and Willow teams in order to drive our research.

We are a young and agile startup: joining us now is the opportunity to grow with the company.