Search Results for author:
Found 27 papers, 8 papers with code
Distilled Gradual Pruning with Pruned Fine-tuning
Neural Networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments.
Ranked #25 on
Image Classification
on CIFAR-10
(Top-1 Accuracy metric)
U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts
Document Layout Analysis, which is the task of identifying different semantic regions inside of a document page, is a subject of great interest for both computer scientists and humanities scholars as it represents a fundamental step towards further analysis tasks for the former and a powerful tool to improve and facilitate the study of the documents for the latter.
Federated Learning for Data and Model Heterogeneity in Medical Imaging
In this paper, we exploit the data and model heterogeneity simultaneously, and propose a method, MDH-FL (Exploiting Model and Data Heterogeneity in FL) to solve such problems to enhance the efficiency of the global model in FL.
Masked Transformer for image Anomaly Localization
Image anomaly detection consists in detecting images or image portions that are visually different from the majority of the samples in a dataset.
Efficient few-shot learning for pixel-precise handwritten document layout analysis
Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription.
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application
Taking medicines is a fundamental aspect to cure illnesses.
Analyzing EEG Data with Machine and Deep Learning: A Benchmark
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology.
Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals
The latter conditions signal-based features in the visual domain to completely replace visual data.
SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoders
Improving existing neural network architectures can involve several design choices such as manipulating the loss functions, employing a diverse learning strategy, exploiting gradient evolution at training time, optimizing the network hyper-parameters, or increasing the architecture depth.
3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition
Estimating the 3D pose of a hand from a 2D image is a well-studied problem and a requirement for several real-life applications such as virtual reality, augmented reality, and hand gesture recognition.
Drone swarm patrolling with uneven coverage requirements
The paper first defines a proper learning model for a single drone, and then extends it to the case of multiple drones both with greedy and cooperative strategies.
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
We present a transformer-based image anomaly detection and localization network.
Ranked #9 on
Anomaly Detection
on BTAD
(using extra training data)
Is It a Plausible Colour? UCapsNet for Image Colourisation
Different from existing works relying on convolutional neural network models pre-trained with supervision, we cast such colourisation problem as a self-supervised learning task.
Image Anomaly Detection by Aggregating Deep Pyramidal Representations
Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class.
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities.
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution
Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data.
An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks
In this paper we focus on this challenge and introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications.
Video-Based Convolutional Attention for Person Re-Identification
In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras.
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator
In the last decade many different algorithms have been proposed to track a generic object in videos.
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations
Single Image Super-Resolution (SISR) aims to generate a high-resolution (HR) image of a given low-resolution (LR) image.
Image anomaly detection with capsule networks and imbalanced datasets
Image anomaly detection consists in finding images with anomalous, unusual patterns with respect to a set of normal data.
Deep Temporal Analysis for Non-Acted Body Affect Recognition
In this paper, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided.
Aggregating Deep Pyramidal Representations for Person Re-Idenfitication
A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation.
Ranked #18 on
Person Re-Identification
on Market-1501
Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition
In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language.
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding
First, a dictionary of sparse atoms is learned using patches extracted from single person images.
The UMCD Dataset
In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks.
Wide-Slice Residual Networks for Food Recognition
We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition.
