361 citations · 518 across the 20 of their papers we have counts for
12 papers · 1 filter
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
Subhankar Roy, Riccardo Volpi, Gabriela Csurka +1
Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensi…
Reliability in Semantic Segmentation: Are We on the Right Track?
Pau de Jorge, Riccardo Volpi, Philip Torr +1
Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance fo…
Semantic Image Segmentation: Two Decades of Research
Gabriela Csurka, Riccardo Volpi, Boris Chidlovskii
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. T…
On the Road to Online Adaptation for Semantic Image Segmentation
Riccardo Volpi, Pau de Jorge, Diane Larlus +1
We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goa…
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
Gabriela Csurka, Riccardo Volpi, Boris Chidlovskii
Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. Yet, the stat…
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
Riccardo Volpi, Diane Larlus, Grégory Rogez
Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature - the well-known "catastrophic forgetti…
Explainable Deep Classification Models for Domain Generalization
Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi +5
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object…
Learning Unbiased Representations via Mutual Information Backpropagation
Ruggero Ragonesi, Riccardo Volpi, Jacopo Cavazza +1
We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attribut…
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi +1
We investigate and characterize the inherent resilience of conditional Generative Adversarial Networks (cGANs) against noise in their conditioning labels, and exploit this fact in…
Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener +3
We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source…
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese +1
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabele…
Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines
Matteo Zanotto, Riccardo Volpi, Alessandro Maccione +3
The retina is a complex nervous system which encodes visual stimuli before higher order processing occurs in the visual cortex. In this study we evaluated whether information about…