Unsupervised cross domain detection and retrieval from scarce data for monitoring of images in social media feeds
Eros Fani (Ph.D. Student)
Social media feeds us every day with an unprecedented amount of visual data. Conservative estimates indicate that roughly 10^1‐10^2M unique images are shared everyday on Twitter, Facebook and Instagram. Images are uploaded by various actors, from corporations to political parties, institutions, entrepreneurs and private citizens. For the sake of freedom of expression, control over their content is limited, and their vast majority is uploaded without any textual description of their content. Their sheer magnitude makes it imperative to use algorithms to monitor, catalog and in general make sense of them, finding the right balance between protecting the privacy of citizens and their right of expression, while fighting illegal and hate content. This in most cases boils down to the ability to automatically associate as many tags as possible to images, which in turns means determining which objects are present in a scene. This PhD project will develop algorithms to automatically tag images from social media feeds and classify them with respect to their content, developing algorithms for detection and content‐ based image retrieval able to work robustly when it is not possible to make strong hypotheses on the visual domain where the incoming test image has been acquired.
|Primary Host:||Barbara Caputo (Politecnico di Torino & Italian Institute of Technology)|
|Exchange Host:||Bernhard Nessler (Johannes Kepler University Linz)|
|PhD Duration:||01 November 2021 - 31 October 2024|
|Exchange Duration:||01 February 2024 - 01 August 2024 - Ongoing|