Search Results for author:
Found 23 papers, 4 papers with code
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
The classification of distracted drivers is pivotal for ensuring safe driving.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset
Second, we adapted several state of the art approaches based on deep learning to fit PRISMA hyperspectral data and then assessed, quantitatively and qualitatively, the performance in this new scenario.
Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion
This advance opens up new possibilities for generating captions that are more suitable for the training of both vision-language and captioning models.
Unsupervised Deep Learning-based clustering for Human Activity Recognition
A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities.
A deep scalable neural architecture for soil properties estimation from spectral information
In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures.
A health telemonitoring platform based on data integration from different sources
The management of people with long-term or chronic illness is one of the biggest challenges for national health systems.
Semi-supervised cross-lingual speech emotion recognition
Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques.
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels
In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation.
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition
The final aim of our work is the definition and implementation of a platform that integrates datasets of inertial signals in order to make available to the scientific community large datasets of homogeneous signals, enriched, when possible, with context information (e. g., characteristics of the subjects and device position).
Composition and Style Attributes Guided Image Aesthetic Assessment
In this paper we propose a method for the automatic prediction of the aesthetics of an image that is based on the analysis of the semantic content, the artistic style and the composition of the image.
Personalization in Human Activity Recognition
In the recent years there has been a growing interest in techniques able to automatically recognize activities performed by people.
Disentangling Image Distortions in Deep Feature Space
In this work we take a further step in the direction of a broader understanding of such property by analyzing the capability of deep visual representations to intrinsically characterize different types of image distortions.
Multitask Painting Categorization by Deep Multibranch Neural Network
In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation.
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition.
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks.
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones
Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria.
On the Use of Deep Learning for Blind Image Quality Assessment
We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task.
Visual descriptors for content-based retrieval of remote sensing images
Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted.
Evaluating color texture descriptors under large variations of controlled lighting conditions
The recognition of color texture under varying lighting conditions is still an open issue.
IAT - Image Annotation Tool: Manual
The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications.
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task.
Remote sensing image classification exploiting multiple kernel learning
Experimental results on publicly available datasets demonstrate the feasibility of the proposed approach.
