Andrea Palazzi

I am a senior engineer at Nomitri, where I work on Deep Learning applied to Computer Vision.

I got my Ph.D. at AimageLab, at the University of Modena and Reggio Emilia, in Italy. I worked under the supervision of Prof. Rita Cucchiara.

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I work in the Machine Learning (ML) team, where we deliver to the other teams the machine learning models on which company products are grounded. Main responsibilities:

• All phases of models life cycle: problem framing, data collection, model design, training and validation; finally quantization for on-device deployment.
• Design, development and maintenance of the internal deep learning software infrastructure as well as of its continuous integration (CI) pipelines.
• Data engineering tasks, e.g. data wrangling and dataset exploration, interface with SQL database, quality inspection for data coming from annotation providers.

During my PhD I mainly worked on a variety of topics, including driver’s gaze prediction, image and video saliency, synthetic data, differentiable rendering, object pose estimation and image generation. Representative papers are highlighted.

Self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be integrated into a deep learning based image generation framework.

To overcome the lack of surveillance data with tracking, body part and occlusion annotations we exploit the photo-realism of modern videogames to create a vast Computer Graphics dataset (~500.000 frames, ~ 10 million body poses) for people tracking in urban scenarios.

We introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information.

We introduce a dataset of human fixations while driving, and a model to predict them given an urban scene.

A dataset with matched localization of vehicles from both camera car and birdseye view, created from computer games. And a baseline model for mapping locations across views.

We study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at? Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset.

We propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers’ gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data.

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