I recently gave a talk at “The Law of Big Data” seminar series @UniPD (you can watch the video on YouTube).
How AI and big data are affecting our society, culture and laws? Many are claiming that data is the “new oil”, and the data-driven / machine-learning paradigm is changing how we address many different problems. Self-driving cars, robot caregivers and chatbot platforms are really happening, while they were only popular sci-fi topics until a few years ago. The aim of this lecture is to introduce the key concepts that have led to these results, to highlight what are the main challenges and open problems, thus trying to unveil what’s in the box.
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.
In the past two weeks I have been involved as computer vision project mentor in the Stanford Artificial Intelligence Laboratory’s OutReach Summer program (SAILORS). SAILORS is a summer camp for high school girls and it is intended to increase diversity in the field of AI. SAILORS aims to teach technically rigorous AI concepts in the context of societal impact.
Check out SAILORS blog to know more about the program. SAILORS was also recently featured in Wired.
We gave a tutorial on “Image Tag Assignment, Refinement and Retrieval” at CVPR 2016, based on our survey. The focus is on challenges and solutions for content-based image retrieval in the context of online image sharing. We present a unified review on three problems: tag assignment, refinement, and tag-based image retrieval.
The slides are available on this page.
We gave a tutorial on “Image Tag Assignment, Refinement and Retrieval” at ACM MM 2015, based on our recent survey. Our tutorial focuses on challenges and solutions for content-based image retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems: tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, and recognize their merits and limitations.
We provided also an hands-on session with the main methods, software and datasets. All data, code and slides are online at: http://www.micc.unifi.it/tagsurvey
I have just given a tutorial on kNN at the Stanford Artificial Intelligence Laboratory’s Outreach Summer program (SAILORS). SAILORS is designed to expose high school students in underrepresented populations to the field of Artificial Intelligence.
The slides are available on this page and the Matlab code is also available for download. This is an updated version of the code used in class and should work also on Octave.
Lorenzo Seidenari and I gave the tutorial “Hands on Advanced Bag-of-Words Models for Visual Recognition” at the ICPR 2014 conference (August 24, Stockholm, Sweden).
All materials – i.e. slides, Matlab code, images and features – and more details can still be found on this webpage.
University of Florence
Course on Multimedia Databases – 2013/14 (Prof. A. Del Bimbo)
Instructors: Lamberto Ballan and Lorenzo Seidenari
Goal
The goal of this laboratory is to get basic practical experience with image classification. We will implement a system based on bag-of-visual-words image representation and will apply it to the classification of four image classes: airplanes, cars, faces, and motorbikes.
We will follow the three steps:
- Load pre-computed image features, construct visual dictionary, quantize features
- Represent images by histograms of quantized features
- Classify images with Nearest Neighbor / SVM classifiers
Getting started
- Download excercises-description.pdf
- Download lab-bow.zip (type the password given in class to uncompress the file) including the Matlab code
- Download 4_ObjectCategories.zip including images and precomputed SIFT features; uncompress this file in lab-bow/img
- Download 15_ObjectCategories.zip including images and precomputed SIFT features; uncompress this file in lab-bow/img
- Start Matlab in the directory lab-bow/matlab and run exercises.m
MICC laboratories, Florence, 31th October 2013 (10.15-13.15). Course on Multimedia Databases (DBMM) – laboratory lecture.
- Goal: logo recognition in web images.
- Dataset/testset: find 4 different logos vs 110 images.
- Evaluation metrics: recognition performances will be evaluated in terms of mean Average Precision (mAP).
Instructors: Lamberto Ballan, Lorenzo Seidenari.
Download Software & Dataset 
(* based on VLFeat library by A. Vedaldi)
Final results (ranking): http://goo.gl/o5DCG5
Lorenzo Seidenari and I will give a tutorial named “Hands on Advanced Bag-of-Words Models for Visual Recognition” at the forthcoming ICIAP 2013 conference (September 9, Naples, Italy). All materials (slides, Matlab code, etc.) and more details can be found on this webpage.
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