AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /
By Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Abstract

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines. This problem will never be solved without algorithms able to adapt and generalize across visual domains. Within the context of domain adaptation and generalization, this paper focuses on the predictive domain adaptation scenario, namely the case where no target data are available and the system has to learn to generalize from annotated source images plus unlabeled samples with associated metadata from auxiliary domains. Our contribution is the first deep architecture that tackles predictive domain adaptation, able to leverage over the information brought by the auxiliary domains through a graph. Moreover, we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time, in a continuous domain adaptation scenario. Experiments on three benchmark databases support the value of our approach.

More publications

Disentangling Monocular 3D Object Detection

By Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2019 /

Seamless Scene Segmentation

By Lorenzo Porzi, Samuel Rota Bulò, Aleksander Colovic, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

By Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulò, Nicu Sebe, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

Deep Single Image Camera Calibration with Radial Distortion

By Manuel López-Antequera, Roger Marı́, Pau Gargallo, Yubin Kuang, Javier Gonzalez-Jimenez, Gloria Haro
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019 /

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View

By Albert Pumarola, Antonio Agudo, Lorenzo Porzi, Alberto Sanfeliu, Vincent Lepetit, Francesc Moreno-Noguer
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

Boosting Domain Adaptation by Discovering Latent Domains

By Massimilano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018 /

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

By Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2017 /

AutoDIAL: Automatic DomaIn Alignment Layers

By Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
International Conf. on Computer Vision (ICCV) 2017 /

Loss Max-Pooling for Semantic Image Segmentation

By Samuel Rota Bulò, Gerhard Neuhold, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2017 /

Online Learning with Bayesian Classification Trees

By Samuel Rota Bulò, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2016 /

Dropout Distillation

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Intern. Conf. on Machine Learning (ICML) 2016 /