“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.
In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.
Featured articles:
- De Weerdt, B. et al., “Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding” (https://ieeexplore.ieee.org/abstract/document/10094712)
- Yang X. et al., “Relevance Propagation through Deep Conditional Random Fields” (https://ieeexplore.ieee.org/abstract/document/10095075)
- Stergiou A. et al., “Play it back: Iterative attention for audio recognition” (https://ieeexplore.ieee.org/abstract/document/10096532, developed at the University of Bristol)
Gisteren hebben we tijdens een zonovergoten avond het ecosysteem van Green Energy Park vzw en FACT – VUB samengebracht voor een interessant netwerkevent.
Een inspirerende uitleg over onze ambities, de visie van Siemens over het belang van intense samenwerking, overtuigende pitches van de onderzoeksgroepen van FACT – VUB, een infosessie over het Interreg-project EMR H2-HUBs, verschillende demo’s … een mooi programma waar iedereen zijn gading in kon vinden.
De avond werd afgesloten onder een stralende zon waardoor het netwerken net dat tikkeltje aangenamer was.
Bedankt aan onze sprekers Inne Peersman, Siperius Yves, Maarten Messagie, Cedric De Cauwer, Gabriela Barrera, Md Sazzad Hosen, Christof Devriendt, Aurélie Bellemans, Lukas Berger, Tom Van Assche, Johan Stiens, Hubert Rahier en onze demostandhouders Priva, Fluvius, Allspark, Flux50, ABB, EVERGi, ETRO VUB, Physical Chemistry and Polymer Science en VUB Architectural Engineering.
- The lab receives the MC6800 development kit – the logic state analyser, was the mostly used development instrument, followed by programming in assembler. The system was interfaced to a mechanical teletype.
- First thesis work in microelectronics technology on CCD-MNOS combination. Roger Vounckx join the lab.
- An analogue bucket brigade system is built with discrete components.
- Worldwide first BSPM measurement system with 128 channels based on MSI ICs.
- Marc Nyssen joins the lab (August 1975).
- New instrument designs – Digital correlator and Histogram measurement system, Fourier transformer.
- CDC central ULB-VUB computer facility exclusively for numerical calculus, with punch card readers as interface; FORTRAN is the dominating programming language.
Jan Cornelis – unoffcial HoD ad interim
Nicolas Ospitia Patino has successfully defended his Ph.D. dissertation obtaining the greatest honors and congratulations from the jury! It’s been an incredible journey of dedication, hard work, and personal growth. He did his PhD in collaboration between MeMC and ETRO, supervised by Prof. Dimitris Aggelis (MeMC) and Prof. Johan Stiens, (ETRO), Congratulations to Nicolas for this result.
On July 2 2021 at 16.30 Abel Diaz Berenguer will defend his PhD entitled “Learning to predict human behavior in crowded scenes”.
Automatically understanding human behavior is one of the most fundamental research topic towards socially aware vision-based autonomous systems. There is an increasing interest in incorporating the social signal perspective into the learning systems pipeline. This dissertation focuses on developing and incorporating computational mechanisms of Computer Vision and Machine Learning to analyze and predict human behavior in crowded scenes automatically. Our research specifically addresses public safety assisted by autonomous video surveillance systems aiming to decrease the human labor dedicated to video monitoring.
Our research efforts concentrate on the information processing pipeline for learning systems that cope with human trajectory prediction and human behavior analysis in crowded scenes. We contribute to human trajectory prediction in crowded scenes with
(i) a novel latent variable model aware of the human-human and human-contextual interactions to predict plausible trajectories. Furthermore,
(ii) a novel latent location-velocity recurrent model that predicts future variable and feasible trajectories. Towards human anomalous behavior detection, we adopt two unsupervised approaches based on the scene dominant behavior and trajectories underlying properties to address trajectory-based anomaly detection. Besides, we contribute with
(iii) a supervised approach capable of attaining discriminative sequence-based feature representations to recognize whether video sequences depict violent human behavior.
Extensive experiments on publicly available datasets, demonstrate the effectiveness of our proposals.
The motivation letter is very important and should clearly describe your background, experience, and goals in your career.