Davide Abati

I am a senior engineer at Qualcomm AI Research, working on Deep Learning for 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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Research

The first part of my Ph.D. has been devoted to computer vision for automotive applications. More recently I developed a strong interest in deep generative models, and their application in out-of-distribution detection and continual learning. Representative papers are highlighted.

Conditional Channel Gated Networks for Task-Aware Continual Learning
Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi
CVPR, 2020, to appear (Oral presentation)
arXiv  /  bibtex

A continual learning model based on the framework of conditional computation.

Latent Space Autoregression for Novelty Detection
Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
CVPR, 2019
arXiv  /  code  /  poster  /  bibtex

Applying autoregression in an autoencoder's latent space increases its out-of-distribution detection capabilities.

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara
AISTATS, 2019
arXiv  /  poster  /  bibtex

We propose a local graph operator resembling convolution as applied in modern neural nets. Can be applied on skeletons, documents, images.

Self-Supervised Optical Flow Estimation by Projective Bootstrap
Stefano Alletto, Davide Abati, Simone Calderara, Rita Cucchiara, Luca Rigazio
IEEE Transactions on Intelligent Transportation Systems, 2018
video  /  bibtex

A self-supervised model for optical flow estimation in automotive settings. The flow field is initialized with the prediction of a single projective matrix.

Predicting the Driver's Focus of Attention: the DR(eye)VE Project
Andrea Palazzi, Davide Abati, Francesco Solera, Simone Calderara, Rita Cucchiara
TPAMI, 2018
arXiv  /  dataset  /  code  /  video  /  bibtex

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

Learning to Map Vehicles into Bird’s Eye View
Andrea Palazzi, Guido Borghi, Davide Abati, Simone Calderara, Rita Cucchiara
International Conference on Image Analysis and Processing, 2017
Best paper honorable mention
arXiv  /  dataset  /  code  /  video  /  bibtex

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.

Other

ConvLSTM_pytorch (with ndrplz)

google-drive-downloader (with ndrplz)

Sometimes I play guitar


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