Search Results for author: Paolo Rota

Found 10 papers, 4 papers with code

Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation

1 code implementation26 Mar 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, Elisa Ricci

This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.

Contrastive Learning Incremental Learning +4

Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

1 code implementation1 Feb 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci

To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.

Incremental Learning Semantic Segmentation

Variational Structured Attention Networks for Deep Visual Representation Learning

1 code implementation5 Mar 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).

Depth Estimation Representation Learning +1

Curriculum Learning: A Survey

no code implementations25 Jan 2021 Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs.

BIG-bench Machine Learning

Variational Structured Attention Networks for Dense Pixel-Wise Prediction

1 code implementation1 Jan 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.

Low-Budget Label Query through Domain Alignment Enforcement

no code implementations1 Jan 2020 Jurandy Almeida, Cristiano Saltori, Paolo Rota, Nicu Sebe

Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.

Unsupervised Domain Adaptation

Curriculum Self-Paced Learning for Cross-Domain Object Detection

no code implementations15 Nov 2019 Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e. g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN.

Domain Adaptation object-detection +1

Indirect Match Highlights Detection with Deep Convolutional Neural Networks

no code implementations2 Oct 2017 Marco Godi, Paolo Rota, Francesco Setti

Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience.

The S-Hock Dataset: Analyzing Crowds at the Stadium

no code implementations CVPR 2015 Davide Conigliaro, Paolo Rota, Francesco Setti, Chiara Bassetti, Nicola Conci, Nicu Sebe, Marco Cristani

In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc.

Head Pose Estimation Sociology

Cannot find the paper you are looking for? You can Submit a new open access paper.