1 code implementation • 29 Feb 2024 • Niccolò Cavagnero, Gabriele Rosi, Claudia Cuttano, Francesca Pistilli, Marco Ciccone, Giuseppe Averta, Fabio Cermelli
To fill this gap, we propose Prototype-based Efficient MaskFormer (PEM), an efficient transformer-based architecture that can operate in multiple segmentation tasks.
1 code implementation • 29 Jun 2022 • Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi
However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.
1 code implementation • 17 Jun 2022 • Niccolò Cavagnero, Luca Robbiano, Barbara Caputo, Giuseppe Averta
In the last decade, most research in Machine Learning contributed to the improvement of existing models, with the aim of increasing the performance of neural networks for the solution of a variety of different tasks.
1 code implementation • 6 Nov 2022 • Gabriele Goletto, Mirco Planamente, Barbara Caputo, Giuseppe Averta
To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities.
no code implementations • 28 May 2022 • Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone, Giuseppe Averta, Tatiana Tommasi, Paolo Rech
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving.
no code implementations • 9 Sep 2022 • Mirco Planamente, Gabriele Goletto, Gabriele Trivigno, Giuseppe Averta, Barbara Caputo
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition.
no code implementations • 7 Mar 2023 • Gabriele Tiboni, Andrea Protopapa, Tatiana Tommasi, Giuseppe Averta
Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability.
no code implementations • 6 Oct 2023 • Niccolò Cavagnero, Luca Robbiano, Francesca Pistilli, Barbara Caputo, Giuseppe Averta
Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models.
no code implementations • 6 Oct 2023 • Francesca Pistilli, Giuseppe Averta
Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications.
no code implementations • 20 Feb 2024 • Claudia Cuttano, Antonio Tavera, Fabio Cermelli, Giuseppe Averta, Barbara Caputo
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware.
no code implementations • 5 Mar 2024 • Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro, Giuseppe Averta
Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once.
no code implementations • 15 Apr 2024 • Gabriele Rosi, Claudia Cuttano, Niccolò Cavagnero, Giuseppe Averta, Fabio Cermelli
Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices.