Deep learning

HPC – Technologies and Models for Smart Cities and the Digital Society

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.

Benchmarking Algorithms for Food Localization and Semantic Segmentation

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.

Fast Scene Understanding

We propose a network architecture to perform efficient scene understanding. This work presents three main …

Single Image Dehazing

In this work we propose HR-Dehazer, a novel and accurate method for image dehazing. An …

Food-475 Database

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.

Real Time Semantic Segmentation

Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling …

Food 524 Database

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.

Art² Gallery

Sabato 1 ottobre 2016 abbiamo partecipato al Meet Me Tonight 2016. I visitatori dello stand …

Image Aesthetics

In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict …

Large Age-Gap Face Verification

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.

Content-based retrieval of remote sensing images

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.