The Italian Institute of Technology (IIT) and the University of Genoa are offering 4 PhD scholarships on Computational Vision, Automatic Recognition, and Learning. Research and training activities will be jointly conducted between the DITEN Department of the University of Genoa and IIT infrastructures in Genoa, at the PAVIS - Pattern Analysis and Computer Vision Research line. The PhD program will focus on various research topics, including 3D scene understanding, multi-modal learning, self-supervised and unsupervised deep learning, generative models for human and scene generation, novel graph operators for learning on large-scale and temporal data, and domain adaptation and generalization.

Materials with complex appearances, like textiles and foodstuffs, pose challenges for conventional theories of vision. How does the brain learn to see properties of the world—like the glossiness of a surface—that cannot be measured by any other senses? Recent advances in unsupervised deep learning may help shed light on material perception. I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model makes patterns of errors in its perception of material that follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how many perceptual dimensions form, in material perception and beyond.