We bring our expertise in computer vision and machine learning on a variety of projects.
Causality for Computer Vision
Representation learning approaches provide remarkable results but are often based on complex architectures requiring huge datasets and unsustainable computations. Despite this gargantuan amount of data and compute, state-of-the-art methods often generalize poorly in practice due to slight changes in between the training and test distribution. Causal machine learning offers an elegant approach to study this problem but it does not scale as well as modern deep learning. Our goal in this project is to explore how causal structure in the data distribution can inform the design of novel forms of inductive bias, making AI models easier to train, more sustainable, and more reliable in solving real world problems.
Funded by PON-REACT EU.
UniGe | LCSL @ MaLGa
Francesco Locatello, Amazon AWS
Multi-resolution signal processing
Shearlets are a multi-resolution analysis framework with many suitable properties for it to be applied to the analysis of images and videos. Among these, we mention its ability in characterizing anisotropic structures, and in enhancing signal singularities. For these reasons, we have adopted it in the detection and descriptions of keypoints in images and image sequences. Its robustness to noise (including motion blur and compression artifacts), makes it suitable to address different applications in the signal processing domain.
UniGe | CHarML @ MaLGa
ReferencesD Malafronte, E De Vito, F Odone, Space-time signal analysis and the 3D Shearlet Transform, Journal of Mathematical Imaging and Vision 2018
MA Duval-Poo, N Noceti, F Odone, E De Vito, Scale invariant and noise robust interest points with shearlets, IEEE Transactions on Image Processing 26 (6), 2017
MA Duval-Poo, F Odone, E De Vito, Edges and corners with shearlets, IEEE Transactions on Image Processing, 2015