Search Results - "ROTA, Paolo"
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Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound
Published in IEEE transactions on medical imaging (01-08-2020)“…Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based…”
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Curriculum self-paced learning for cross-domain object detection
Published in Computer vision and image understanding (01-03-2021)“…Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem,…”
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Editorial for the Special Issue on Industrial Machine Learning Applications
Published in Journal of imaging (01-12-2023)“…In the rapidly evolving field of industrial machine learning, this Special Issue on Industrial Machine Learning Applications aims to shed light on the…”
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Optical Monitoring of the Didymos–Dimorphos Asteroid System with the Danish Telescope around the DART Mission Impact
Published in The planetary science journal (01-12-2023)“…Abstract The NASA’s Double-Asteroid Redirection Test (DART) was a unique planetary defence and technology test mission, the first of its kind. The main…”
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Curriculum Learning: A Survey
Published in International journal of computer vision (01-06-2022)“…Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements…”
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Cut Quality Estimation in Industrial Laser Cutting Machines: A Machine Learning Approach
Published in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (01-06-2019)“…The use of machine learning models to improve industrial production quality is becoming more popular year after year. The main reason is the huge data…”
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Conference Proceeding -
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Low-budget label query through domain alignment enforcement
Published in Computer vision and image understanding (01-09-2022)Get full text
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Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation
Published in IEEE transactions on pattern analysis and machine intelligence (01-02-2023)“…A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e., the tendency of neural networks to fail to preserve the knowledge…”
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Variational Structured Attention Networks for Deep Visual Representation Learning
Published in IEEE transactions on image processing (02-03-2022)“…Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface…”
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Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Published in IEEE transactions on multimedia (01-01-2023)“…Over the past years, semantic segmentation, similar to many other tasks in computer vision, has benefited from the progress in deep neural networks, resulting…”
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Simplifying open-set video domain adaptation with contrastive learning
Published in Computer vision and image understanding (01-04-2024)“…In an effort to reduce annotation costs in action recognition, unsupervised video domain adaptation methods have been proposed that aim to adapt a predictive…”
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AutoLabel: CLIP-based framework for Open-Set Video Domain Adaptation
Published in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (01-06-2023)“…Open-set Unsupervised Video Domain Adaptation (OU-VDA) deals with the task of adapting an action recognition model from a labelled source domain to an…”
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Conference Proceeding -
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Dual-Head Contrastive Domain Adaptation for Video Action Recognition
Published in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (01-01-2022)“…Unsupervised domain adaptation (UDA) methods have become very popular in computer vision. However, while several techniques have been proposed for images, much…”
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Conference Proceeding -
14
Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures
Published in Pattern recognition (01-12-2016)“…We propose a supervised learning approach to automatic quantification of cell populations in flow cytometric samples. One sample contains up to millions of…”
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The S-Hock dataset: A new benchmark for spectator crowd analysis
Published in Computer vision and image understanding (01-06-2017)“…•Spectator crowd is a particular kind of crowd where people are “interested in watching something specific that they came to see”.•We present a novel dataset…”
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Einstein, Planck and Vera Rubin: Relevant Encounters Between the Cosmological and the Quantum Worlds
Published in Frontiers in physics (16-02-2021)“…In Cosmology and in Fundamental Physics there is a crucial question like: where the elusive substance that we call Dark Matter is hidden in the Universe and…”
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On the Use of the Platelet Activity State Assay for the In Vitro Quantification of Platelet Activation in Blood Recirculating Devices for Extracorporeal Circulation
Published in Artificial organs (01-10-2016)“…We designed an experimental setup to characterize the thrombogenic potential associated with blood recirculating devices (BRDs) used in extracorporeal…”
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Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?
Published in 2016 23rd International Conference on Pattern Recognition (ICPR) (01-12-2016)“…The pervasive availability of the Internet, coupled with the development of increasingly powerful technologies, has led digital images to be the primary source…”
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Conference Proceeding -
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The S-HOCK dataset: Analyzing crowds at the stadium
Published in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (01-06-2015)“…The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy…”
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Conference Proceeding -
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Collaborative creativity: The Music Room
Published in Personal and ubiquitous computing (01-06-2014)“…In this paper, we reflect on our experience of designing, developing and evaluating interactive spaces for collaborative creativity. In particular, we are…”
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