HPC is a foundation uniting 51 interdisciplinary public and private organizations, driving substantial and sustainable innovations across various fields, from basic research to computational and experimental sciences, supporting education, and advocating responsible data management for open science.
OnFoods is a foundation that brings together, coordinates and amplifies the work of 26 public and private organisations, leaders in scientific research and sustainable innovation of food systems.
This research focuses on the exploration of the most widely adopted libraries specifically designed for data augmentation in computer vision tasks. Through a curated taxonomy, we present an organized classification of different approaches employed by these libraries, along with accompanying application examples.
U-WeAr: User Recognition on Wearable Devices through Arm Gesture The use of wearable devices equipped …
We address the task of classifying car images at multiple levels of detail, ranging from …
We create a new dataset composed of 120,000 images of 50 diverse food categories. The images are accurately annotated with pixel-wise annotations. The dataset is augmented with the same 5,000 images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur.
We propose a network architecture to perform efficient scene understanding. This work presents three main …
In this project we use deep learning with the aim of automatically finding products in grocery store shelves.
In this work we propose HR-Dehazer, a novel and accurate method for image dehazing. An …
Food-475 database is one of the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases.
Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling …
In this project we propose a new deep multibranch neural network to solve the tasks …
Food524DB is the largest publicly available food dataset with 524 food classes and 247,636 images by merging food classes from existing datasets in the state of the art. This database can be used for food recognition. The database is composed of 247,636 images belonging to 524 food categories.
In this project we present a method for logo recognition based on deep learning. Our recognition …
Sabato 1 ottobre 2016 abbiamo partecipato al Meet Me Tonight 2016. I visitatori dello stand …
In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict …
In this work we investigate the use of deep learning for distortion-generic blind image quality assessment.
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In this paper we present a three-stage method for the estimation of the color of the …
This work introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old.
We present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global, local, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes.