New class on cognitive computing, applied machine learning and vision

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By , February 22, 2018

This semester, together with Alessandro Sperduti, we are teaching a new course on cognitive computing, applied machine learning and computer vision. This new class is motivated by the recent revolution of cloud-enabled artificial intelligence services. We will give an introductory overview of deep learning, the main fields of application of machine learning and representation learning, with a particular emphasis on visual recognition problems. The class is offered to master students in computer science and data science, but it is also open to others (e.g. undergrads in CS or students from the school of engineering). More info: course webpage @UniPD, course presentation.

Deep Learning, Pattern Recognition and Vision, which direction?

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By , January 29, 2018

I have been invited to contribute to the panel “Deep Learning, Pattern Recognition and Vision, which direction?” during the CVPL day in Modena, Italy, on “Why is Deep Learning so cool?” [slides available online]. The event has been opened by Naftali Tishby with a very interesting talk about “Information Theory of Deep Learning”. A similar talk is also available on YouTube.

European Researchers’ Night: “Science is wonder-ful!”

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By , September 29, 2017

This 26 and 27 September 2017, I was involved in the kickoff event for European Researchers’ Night in Brussels #MSCAnight. With thousands of visitors, most of which were children, it was a smashing success! I have presented my research on machine learning and visual perception, and talked about visual illusions and teaching computers to see.

More about the event: https://goo.gl/QNkxgY, more about my work and some nice pictures.

About Me

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By , September 3, 2017

Lamberto BallanI am an Assistant Professor (tenure track) of Computer Science at University of Padova, Italy, where I lead the Visual Intelligence and Machine Perception (VIMP) group. Previously, I was a senior postdoctoral researcher in the AI Lab at Stanford – working with L. Fei-Fei and S. Savarese – supported by a prestigious Marie Curie Fellowship from the European Commission.

I earned my Laurea (M.S.) and Ph.D. degrees in computer engineering in 2006 and 2011, both from the Univ. of Florence (Italy), advised by A. Del Bimbo. I also spent time at the Image, Data, Signal Department at Telecom ParisTech (France) in 2010.

My primary research area is computer vision, closely integrated with applied machine learning and multimedia. The main focus of my current research is on designing learning algorithms that make the most effective use of contextual knowledge in presence of sparse and noisy data. Some examples are image and video annotation, large-scale visual recognition, web mining, social media analysis.

Research Group / CV / Google ScholarDBLP / ORCID

Aloha CVPR’17: I will be giving two talks on July 26

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By , July 22, 2017

CVPR is growing exponentially. This year in the beautiful Honolulu there are approx. 5K attendees. First, I will present our paper on “Localization of JPEG double compression through multi-domain convolutional neural networks” (Amerini, Uricchio, Ballan, Caldelli) at the CVPR’17 Workshop on Media Forensics. Then, I will give a keynote talk on “Exploiting noisy web data for large-scale visual recognition” at the CVPR’17 Workshop on Visual Understanding by Learning from Web Data [slides available online].

New papers on image tagging and webly-supervised action recognition

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By , July 21, 2017

Our paper “Automatic Image Annotation via Label Transfer in the Semantic Space”, by T. Uricchio, myself, L. Seidenari and A. Del Bimbo, has been accepted for publication in Pattern Recognition and is now available online. It is an extended version of our KCCA-based tag propagation model presented in our ICMR’14 paper, containing more experiments and a novel tag denoising procedure.

A few days later, our paper “Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition” has been also accepted for publication in Computer Vision and Image Understanding (CVIU) and is now available online. Here we present a (fully) webly-supervised model for action recognition in videos. This is a joint work with F. Tombari and C. Rupprecht from TUM (Germany).

I will be joining the University of Padova!

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By , February 3, 2017

Univ. of Padova, Palazzo BoHappy to share that I will join the Math & CS Department of the University of Padova as an Assistant Professor (tenure track) of Computer Science, starting in Fall 2017.

The University of Padova is one of Europe’s oldest and most prestigious seats of learning. The University of Padova is ranked first among Italian universities according to most international rankings (ARWU, US-News) and research evaluation agencies (ANVUR) [UniPD at a glance].

Universa Universis Patavina Libertas.

Invited Talk: Sharing Knowledge for Large Scale Visual Recognition

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By , November 18, 2016

In the last months I gave several times this talk about my recent work on knowledge transfer for large-scale visual recognition problems (e.g. Aquifi Inc – Palo Alto, Google ResearchUC Santa Cruz, MICCU. of Parma, U. of Catania, U. of Padova, “Ca’ Foscari” U. Venice). The key idea of this work is to transfer prior contextual knowledge to novel scenes where it is hard to collect large-scale training data [slides available online].

Marie Curie Fellow of the week

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By , November 11, 2016

I have been selected Marie Curie Fellow of the week! Marie Sklodowska-Curie Actions (MSCA) Individual Fellowships are highly prestigious and competitive and are meant to support the best, most promising European researchers.

More info on this Facebook post.

CVIU Special Issue on Computer Vision and the Web

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By , September 14, 2016

cviuI am co-organizing a special issue for the Computer Vision and Image Understanding journal on “Computer Vision and the Web”, together with Shih-Fu Chang (Columbia University), Gang Hua (Microsoft Research Asia), Thomas Mensink (Univ. of Amsterdam), Greg Mori (Simon Fraser Univ.) and Rahul Sukthankar (Google Research). You can see all the details on the call for papers.

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