Author's posts

Israel D Gebru received the ICLR’21 outstanding paper award!

Israel D Gebru, former PhD student in the Perception group (2014-2018), and his co-authors, Alexander Richard, Dejan Markovic, Steven Krenn, Gladstone Alexander Butler, Fernando Torre, and Yaser Sheikh, received the outstanding paper award at the International Conference on Learning Representation (ICLR’21) for their paper “Neural Synthesis of Binaural Speech from Mono Audio.” During his PhD, …

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ACM-TOMM (Nicolas D. Georganas) 2020 Best Paper Award

Xavier Alameda-Pineda and his collaborators from the University of Trento received the 2020 Nicolas D. Georganas Best Paper Award, that recognizes the most significant work in ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM) in a given calendar year: Increasing image memorability with neural style transfer,  vol. 15 Issue 2, January 2019 by A. Siarohin, …

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Paper published in IEEE Transactions on PAMI

The paper Variotional Bayesian Inference for Audio-Visual Tracking of Multiple Speakers has been published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (journal with one of the highest impact score in the category computational intelligence). This work is part of the Ph.D. thesis of Yutong Ban, now with the Computer Science and Artificial …

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Master Internship on Deep Bayesian Filtering

In signal processing and in computer vision, some of the most powerful tracking methods are based on the Kalman filter. The latter belongs to the unsupervised class of machine learning techniques and may well be viewed either as the simplest dynamic Bayesian network (DBN) or as a state-space model: it recursively predicts over time a …

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Deformations in Deep Models for Image and Video Generation

Seminar  by Stéphane Lathuilière, Télécom Paris Friday, 18 October 2019, 14:30 – 15:30, room F107 INRIA Montbonnot Saint-Martin   Abstract: 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 …

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Xavier Alameda-Pineda appointed IEEE Senior Member

Xavier Alameda-Pineda has become an IEEE Senior Member on  February 1st,  2019. The grade of Senior Member requires experience reflecting professional maturity as an engineer, scientist, educator, technical executive, or originator in IEEE-designated fields for a total of 10 years and have demonstrated 5 years of significant performance.

The Kinovis-MST Dataset

The Kinovis Multiple-Speaker Tracking Dataset Data | pdf from arXiv | download | reference The Kinovis multiple speaker tracking (Kinovis-MST) datasets contain live acoustic recordings of multiple moving speakers in a reverberant environment. The data were recorded in the Kinovis multiple-camera laboratory at INRIA Grenoble Rhône-Alpes.  The room size is 10.2 m × 9.9  m × 5.6 …

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Book co-edited by Xavier Alameda-Pineda

New book published by Academic Press (Elsevier), entitled “Multimodal Behavior Analysis in the Wild”, edited by Xavier Alameda Pineda, Elisa Ricci and Nicu Sebe. The book gathers 20 chapters written by 75 researchers from all over the world.  

ACM SIGMM Rising Star Award 2018

The 2018 winner of the prestigious ACM Special Interest Group on Multimedia (SIGMM) Rising Star Award is our colleague Dr. Xavier Alameda-Pineda. The award is given in recognition of his contribution to multimodal social behavior understanding. Congratulations Xavi!

Sparse representation, dictionary learning, and deep neural networks: their connections and new algorithms

Seminar  by Mostafa Sadeghi, Sharif University of Technology, Tehran Tuesday 19 June 2018, 14:30 – 15:30, room F107 INRIA Montbonnot Saint-Martin Abstract. Over the last decade, sparse representation, dictionary learning, and deep artificial neural networks have dramatically impacted on the signal processing and machine learning areas by yielding state-of-the-art results in a variety of tasks, …

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