1 code implementation • ICCV 2023 • Giacomo Zara, Alessandro Conti, Subhankar Roy, Stéphane Lathuilière, Paolo Rota, Elisa Ricci
Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data.
1 code implementation • NeurIPS 2023 • Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota, Yiming Wang, Elisa Ricci
We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.
1 code implementation • 9 May 2023 • Gk Tejus, Giacomo Zara, Paolo Rota, Andrea Fusiello, Elisa Ricci, Federica Arrigoni
In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively.
1 code implementation • CVPR 2023 • Giacomo Zara, Subhankar Roy, Paolo Rota, Elisa Ricci
Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source.
1 code implementation • 9 Jan 2023 • Giacomo Zara, Victor Guilherme Turrisi da Costa, Subhankar Roy, Paolo Rota, Elisa Ricci
In this work we address a more realistic scenario, called open-set video domain adaptation (OUVDA), where the target dataset contains "unknown" semantic categories that are not shared with the source.
no code implementations • 12 Nov 2022 • Hao Tang, Lei Ding, Songsong Wu, Bin Ren, Nicu Sebe, Paolo Rota
The proposed TSDPC is a generic and powerful framework and it has two advantages compared with previous works, one is that it can calculate the number of key frames automatically.
1 code implementation • 11 Oct 2022 • Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding.
Cross-Domain Facial Expression Recognition Facial Expression Recognition (FER) +2
1 code implementation • 26 Jul 2022 • Victor G. Turrisi da Costa, Giacomo Zara, Paolo Rota, Thiago Oliveira-Santos, Nicu Sebe, Vittorio Murino, Elisa Ricci
On the other hand, the performance of a model in action recognition is heavily affected by domain shift.
1 code implementation • 26 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.
1 code implementation • 1 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.
1 code implementation • 5 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).
no code implementations • 25 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.
1 code implementation • 1 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.
no code implementations • 1 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.
no code implementations • 15 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.
no code implementations • 2 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.
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.