1 code implementation • 21 Apr 2022 • Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan, Alessio Del Bue
This paper presents a novel end-to-end method for the problem of skeleton-based unsupervised human action recognition.
no code implementations • 3 Jan 2022 • Shah Nawaz, Jacopo Cavazza, Alessio Del Bue
Zero-shot learning methods rely on fixed visual and semantic embeddings, extracted from independent vision and language models, both pre-trained for other large-scale tasks.
no code implementations • 29 Sep 2021 • Ruggero Ragonesi, Valentina Sanguineti, Jacopo Cavazza, Vittorio Murino
It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples.
no code implementations • 23 Mar 2021 • Federico Marmoreo, Julio Ivan Davila Carrazco, Vittorio Murino, Jacopo Cavazza
We formalize OZSL as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided.
no code implementations • 20 Mar 2021 • Jacopo Cavazza
Given that the latter seamlessly generalize towards unseen classes, while requiring not actual unseen data to be computed, we can perform ZSL inference by augmenting the pool of classification rules at test time while keeping the very same representation we learnt: nowhere re-training or fine-tuning on unseen data is performed.
no code implementations • 5 Feb 2021 • Federico Marmoreo, Jacopo Cavazza, Vittorio Murino
In this paper, we address zero-shot learning (ZSL), the problem of recognizing categories for which no labeled visual data are available during training.
1 code implementation • 21 Jun 2020 • Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan, Alessio Del Bue
This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data.
Ranked #1 on Skeleton Based Action Recognition on MSR ActionPairs
1 code implementation • 13 Mar 2020 • Ruggero Ragonesi, Riccardo Volpi, Jacopo Cavazza, Vittorio Murino
We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data.
no code implementations • 28 Nov 2017 • Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition.
1 code implementation • ICLR 2018 • Pietro Morerio, Jacopo Cavazza, Vittorio Murino
In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains.
no code implementations • 13 Oct 2017 • Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino, Rene Vidal
Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways.
no code implementations • 10 Oct 2017 • Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele, Vittorio Murino, René Vidal
While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization.
no code implementations • 6 Sep 2017 • Jacopo Cavazza, Pietro Morerio, Vittorio Murino
In this work we reduce such complexity to be linear by proposing a novel and explicit feature map to approximate the kernel function.
no code implementations • 3 Aug 2017 • Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions.
no code implementations • 3 Aug 2017 • Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors.
2 code implementations • ICCV 2017 • Pietro Morerio, Jacopo Cavazza, Riccardo Volpi, Rene Vidal, Vittorio Murino
This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem.
no code implementations • 5 Jun 2016 • Jacopo Cavazza, Vittorio Murino
This paper addresses the scalar regression problem through a novel solution to exactly optimize the Huber loss in a general semi-supervised setting, which combines multi-view learning and manifold regularization.
no code implementations • 31 May 2016 • Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
In this paper, we address the new problem of the prediction of human intents.
no code implementations • 2 May 2016 • Andrea Zunino, Jacopo Cavazza, Vittorio Murino
In particular, considering that each human action in the datasets is performed several times by different subjects, we were able to precisely quantify the effect of inter- and intra-subject variability, so as to figure out the impact of several learning approaches in terms of classification performance.
no code implementations • 22 Apr 2016 • Jacopo Cavazza, Andrea Zunino, Marco San Biagio, Vittorio Murino
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only.