Zobrazeno 1 - 10
of 314
pro vyhledávání: '"Milano, Francesco"'
State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines for synthetic training data generation. Both factors limit the applicat
Externí odkaz:
http://arxiv.org/abs/2407.12207
Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class, we need to build a large dataset and retrain the system. To build sp
Externí odkaz:
http://arxiv.org/abs/2311.02734
Recently, methods have been proposed for 3D open-vocabulary semantic segmentation. Such methods are able to segment scenes into arbitrary classes based on text descriptions provided during runtime. In this paper, we propose to the best of our knowled
Externí odkaz:
http://arxiv.org/abs/2309.05448
Autor:
Milano, Francesco
This study presents the development and evaluation of a conversational agent, EcoBot, designed to inform users about their energy habits and persuade them to save more energy when at home, to help fight climate change and energy waste. To reach this
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-332089
Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of
Externí odkaz:
http://arxiv.org/abs/2303.10962
Autor:
Liu, Zhizheng, Milano, Francesco, Frey, Jonas, Siegwart, Roland, Blum, Hermann, Cadena, Cesar
An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obt
Externí odkaz:
http://arxiv.org/abs/2211.13969
Publikováno v:
In Plant Physiology and Biochemistry November 2024 216
Publikováno v:
IEEE Robotics and Automation Letters 2022
Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the training data deviates from the one encountered during the robot's operation
Externí odkaz:
http://arxiv.org/abs/2111.02156
Autor:
Maurelli, Anna Maria, De Leo, Vincenzo, Daniello, Valeria, Calvano, Cosima Damiana, Ciriaco, Fulvio, Milano, Francesco, Ingrosso, Chiara, Cataldi, Tommaso R.I., Di Gioia, Sante, Conese, Massimo, Agostiano, Angela, Catucci, Lucia
Publikováno v:
In Materials Today Chemistry April 2024 37
Autor:
Blum, Hermann, Milano, Francesco, Zurbrügg, René, Siegward, Roland, Cadena, Cesar, Gawel, Abel
Publikováno v:
CoRL 2021 https://openreview.net/forum?id=X2KJq-S11BC
We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are contin
Externí odkaz:
http://arxiv.org/abs/2105.01595