Our article “Cockpit video coding with temporal prediction” byI. Mitrica (MM/IDS et Safran), A. Fiandrotti (MM/IDS), M. Cagnazzo (MM/IDS), C. Ruellan and E. Mercier, has received the Best Paper Award at the “European Workshop on Visual Information Processing” (Rome, Italy, 31/10/2019).
Congrats Iulia !
Download the pyton scripts here.
Three new exciting seminars will be given on June 7th, 2019. The program is the following :
Place : Telecom-ParisTech, 46 rue Barrault, Paris. Room B567.
9h45h Welcome coffee
10h00-11h15 “Deep learning for Super Resolution and Tracking”, by Gianni Franchi, Univ. Paris Sud
11h15-12h30 “Deformation models for image and video generation”, by Stéphane Lathuilière, Univ. Trento (Italy)
12h30-14h Lunch break
14h-15h15 “The Next Big Thing: From Systems to Deep Systems”, by Francesco Banterle, CNR Pisa (Italy)
15h15-16h Coffee, free discussion
“Deep learning for Super Resolution and Tracking”
Cette présentation traitera de deux projets :
Le projet 1 : vise à mélanger des techniques de deep learning et de géostatistique pour faire de la super résolution d’images. L’objectif est d’accéder aux bons résultats du deep learning ainsi que l’incertitude de l’estimateur grâce la géostatistique.
Le projet 2 : vise à suivre des personnes dans une vidéo de foule extrêmement dense. N’ayant pas de base donnée annotée sur ce projet, nous proposerons une technique ou le réseau de neurones apprend tout seul. (En anglais : Self supervised learning).
“Deformation models for image and video generation”
Generating realistic images and videos has countless applications in different areas, ranging from photography technologies to e-commerce business.
Recently, deep generative approaches have emerged as effective techniques for generation tasks. In this talk, we will first present the problem of pose-guided person image generation. Specifically, given an image of a person and a target pose, a new image of that person in the target pose is synthesized. We will show that important body-pose changes affect generation quality and that specific feature map deformations lead to better images.
Then, we will present our recent framework for video generation. More precisely, our approach generates videos where an object in a source image is animated according to the motion of a driving video. In this task, we employ a motion representation based on keypoints that are learned in a self-supervised fashion. Therefore, our approach can animate any arbitrary object without using annotation or prior information about the specific object to animate.
“The Next Big Thing: From Systems to Deep Systems”
The main communities in Computer Science are all shifting from traditional algorithms towards deep-based algorithms where deep learning is extensivelyused to solve everyday problems. Although this is very attractive in terms of quality and speed,the days of end-to-end encoding are numbered because more than a network is needed to achieve a full task. This talk will show a traditional system for 3D reconstruction, how to make it deep, and the making of a from scratch deep system in which deep learning was in the loop from start to finish,
Our article on scalable hologram representation has been accepted into IEEE ICIP’10 conference.
Anas El Rhammad, Patrick Gioia, Antonin Gilles, Marco Cagnazzo, ‘SCALABLE CODING FRAMEWORK FOR A VIEW-DEPENDENT STREAMING OF DIGITAL HOLOGRAMS’
Our special session proposal in ACM International Conference on Distributed Smart Cameras (ICDSC’19) has been accepted!
The special session title is “Trends in Free Navigation Technologies”.
Find here the call for papers
It is an open special session, so you can apply directly through the conference website.
Telecom ParisTech, a CS/EE school of Institut Polytechnique de Paris, is hiring an Associate Professor in Video Analysis and Learning. The position will be located in the Multimedia Team, within the Image, Data, Signal Department (IDS), and the LTCI laboratory.
The Multimedia team has a long activity in the domain of video and image coding and transmission. More recently, video analysis and learning activity have become more and more relevant for the team who runs now a regional study group about Machine and Deep Learning applications to Image and Video compression. The team has the target to expand its activity in this area, and several new and exciting research projects have just been launched, such as research programs in Deep Learning assisted video compression and Learning-based photographic quality evaluation. In this context, and to support the increasing activity of the team, a permanent position in video analysis and learning has been opened.
Applicants are expected to provide an outstanding academic research record and will be encouraged to advise PhD theses, supervise engineers and post-docs, while being actively involved in funded projects and in the activities of the Multimedia team. The teaching activities will take place in the engineer and master tracks at Telecom ParisTech and can be given in English.
Find here more information.
Apply via e-mail.
Three articles have been accepted into IEEE ICASSP :
1) S. Zheng, M. Cagnazzo, M. Kieffer. “CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES”
2) L. Wang, A. Fiandrotti, A. Purica, G. Valenzise, M. Cagnazzo. “ENHANCING HEVC SPATIAL PREDICTION BY CONTEXT-BASED LEARNING”
3) P. Nikitin, M. Cagnazzo, J. Jung. “COMPRESSION IMPROVEMENT VIA REFERENCE ORGANIZATION FOR 2D-MULTIVIEW CONTENT”.
Congrats to Shuo, Li and Pavel.
Our article entitled “Very Low Bitrate Semantic Compression of Airplane Cockpit Screen Content” has been accepted for publication in IEEE Transactions on Multimedia.
Congratulations to Iulia, our first author.