8 citations · 11 across the 23 of their papers we have counts for
13 papers · 1 filter
AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Mirko Zaffaroni, Federico Signoretta, Marco Grangetto +1
Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performan…
If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions
Carlo Alberto Barbano, Luca Molinaro, Massimiliano Ciranni +3
Humans can visualize new and unknown concepts from their natural language description, based on their experience and previous knowledge. Insipired by this, we present a way to exte…
Efficient Progressive Image Compression with Variance-aware Masking
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +2
Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compre…
STanH : Parametric Quantization for Variable Rate Learned Image Compression
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +1
In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a cost function, where controls the trade-off between rate of the quantize…
GABIC: Graph-based Attention Block for Image Compression
Gabriele Spadaro, Alberto Presta, Enzo Tartaglione +3
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alterna…
WiGNet: Windowed Vision Graph Neural Network
Gabriele Spadaro, Marco Grangetto, Attilio Fiandrotti +2
In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of…
Anatomical Foundation Models for Brain MRIs
Carlo Alberto Barbano, Matteo Brunello, Benoit Dufumier +1
Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in…
Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering
Francesco Di Sario, Riccardo Renzulli, Enzo Tartaglione +1
Since the introduction of NeRFs, considerable attention has been focused on improving their training and inference times, leading to the development of Fast-NeRFs models. Despite d…
Multi-target stain normalization for histology slides
Desislav Ivanov, Carlo Alberto Barbano, Marco Grangetto
Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diver…
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti +4
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). I…
AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
Carlo Alberto Barbano, Riccardo Renzulli, Marco Grosso +3
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a st…
Domain Adaptation for Learned Image Compression with Supervised Adapters
Alberto Presta, Gabriele Spadaro, Enzo Tartaglione +2
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet i…
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Guglielmo Gallone, Francesco Iodice, Alberto Presta +16
Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predi…