Search Results for author: Barbara Caputo

Found 83 papers, 36 papers with code

Learning Sequential Descriptors for Sequence-based Visual Place Recognition

1 code implementation8 Jul 2022 Riccardo Mereu, Gabriele Trivigno, Gabriele Berton, Carlo Masone, Barbara Caputo

In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places.

Visual Place Recognition

FreeREA: Training-Free Evolution-based Architecture Search

no code implementations17 Jun 2022 Niccolò Cavagnero, Luca Robbiano, Barbara Caputo, Giuseppe Averta

In the last decade, most research in Machine Learning contributed to the improvement of existing models, with the aim of increasing the performance of neural networks for the solution of a variety of different tasks.

Neural Architecture Search

Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

1 code implementation17 Apr 2022 Antonio Tavera, Edoardo Arnaudo, Carlo Masone, Barbara Caputo

We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e. g., a field of crops and a small vehicle).

Semantic Segmentation

Deep Visual Geo-localization Benchmark

1 code implementation CVPR 2022 Gabriele Berton, Riccardo Mereu, Gabriele Trivigno, Carlo Masone, Gabriela Csurka, Torsten Sattler, Barbara Caputo

In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline.

Data Augmentation

Rethinking Visual Geo-localization for Large-Scale Applications

1 code implementation CVPR 2022 Gabriele Berton, Carlo Masone, Barbara Caputo

Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations.

 Ranked #1 on Image Retrieval on AmsterTime (using extra training data)

Contrastive Learning Image Classification +2

Improving Generalization in Federated Learning by Seeking Flat Minima

1 code implementation22 Mar 2022 Debora Caldarola, Barbara Caputo, Marco Ciccone

Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios.

Domain Generalization Federated Learning +1

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

no code implementations28 Feb 2022 Lidia Fantauzzo, Eros Fani', Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo

For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices.

Autonomous Driving Domain Generalization +3

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

1 code implementation31 Jan 2022 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló, Elisa Ricci, Barbara Caputo

To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift.

Weakly supervised segmentation Weakly-Supervised Semantic Segmentation

Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

no code implementations26 Jan 2022 Riccardo Zaccone, Andrea Rizzardi, Debora Caldarola, Marco Ciccone, Barbara Caputo

data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario.

Federated Learning

Learning Semantics for Visual Place Recognition through Multi-Scale Attention

1 code implementation24 Jan 2022 Valerio Paolicelli, Antonio Tavera, Carlo Masone, Gabriele Berton, Barbara Caputo

In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery.

Visual Place Recognition

A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images

1 code implementation7 Dec 2021 Edoardo Arnaudo, Fabio Cermelli, Antonio Tavera, Claudio Rossi, Barbara Caputo

Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets.

Incremental Learning Knowledge Distillation +3

Incremental Learning in Semantic Segmentation from Image Labels

1 code implementation CVPR 2022 Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, Barbara Caputo

As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally.

Incremental Learning Semantic Segmentation

Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation

1 code implementation22 Oct 2021 Antonio Tavera, Fabio Cermelli, Carlo Masone, Barbara Caputo

The pixel-wise adversarial training is assisted by a novel sample selection procedure, that handles the imbalance between source and target data, and a knowledge distillation strategy, that avoids overfitting towards the few target images.

Autonomous Driving Cross-Domain Few-Shot +3

Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation

1 code implementation22 Oct 2021 Antonio Tavera, Carlo Masone, Barbara Caputo

To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation.

Domain Adaptation Real-Time Semantic Segmentation

Viewpoint Invariant Dense Matching for Visual Geolocalization

1 code implementation ICCV 2021 Gabriele Berton, Carlo Masone, Valerio Paolicelli, Barbara Caputo

Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization.


On the Challenges of Open World Recognitionunder Shifting Visual Domains

1 code implementation9 Jul 2021 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Barbara Caputo

Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones.

Domain Generalization Object Recognition

Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation

1 code implementation5 Jul 2021 Silvia Bucci, Francesco Cappio Borlino, Barbara Caputo, Tatiana Tommasi

Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time.

Contrastive Learning Style Transfer +1

PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition

no code implementations1 Jul 2021 Chiara Plizzari, Mirco Planamente, Emanuele Alberti, Barbara Caputo

In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition.

Action Recognition Domain Generalization +1

Towards Fairness Certification in Artificial Intelligence

no code implementations4 Jun 2021 Tatiana Tommasi, Silvia Bucci, Barbara Caputo, Pietro Asinari

Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life.

BIG-bench Machine Learning Decision Making +1

Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment

no code implementations3 Jun 2021 Mirco Planamente, Chiara Plizzari, Emanuele Alberti, Barbara Caputo

First person action recognition is an increasingly researched topic because of the growing popularity of wearable cameras.

Action Recognition

Detecting Anomalies in Semantic Segmentation with Prototypes

1 code implementation1 Jun 2021 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Barbara Caputo

Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training.

Semantic Segmentation

Cluster-driven Graph Federated Learning over Multiple Domains

no code implementations29 Apr 2021 Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Marco Ciccone, Emanuele Rodolà, Barbara Caputo

Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others.

Federated Learning

A Closer Look at Self-training for Zero-Label Semantic Segmentation

1 code implementation21 Apr 2021 Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, Barbara Caputo

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation.

Semantic Segmentation

Inferring Latent Domains for Unsupervised Deep Domain Adaptation

no code implementations25 Mar 2021 Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Most deep UDA approaches operate in a single-source, single-target scenario, i. e. they assume that the source and the target samples arise from a single distribution.

Unsupervised Domain Adaptation

Boosting Binary Masks for Multi-Domain Learning through Affine Transformations

no code implementations25 Mar 2021 Massimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota Buló

In this work, we provide a general formulation of binary mask based models for multi-domain learning by affine transformations of the original network parameters.

Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

1 code implementation12 Feb 2021 Luca Robbiano, Muhammad Rameez Ur Rahman, Fabio Galasso, Barbara Caputo, Fabio Maria Carlucci

Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world.

Model Selection Neural Architecture Search +1

A Response Retrieval Approach for Dialogue Using a Multi-Attentive Transformer

1 code implementation15 Dec 2020 Matteo A. Senese, Alberto Benincasa, Barbara Caputo, Giuseppe Rizzo

Our approach makes use of a neural architecture based on transformer with a multi-attentive structure that conditions the response of the agent on the request made by the user and on the product the user is referring to.

Prototype-based Incremental Few-Shot Semantic Segmentation

1 code implementation30 Nov 2020 Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo

Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, constrained to the classes of the training set.

Few-Shot Semantic Segmentation Incremental Learning +2

Adaptive-Attentive Geolocalization from few queries: a hybrid approach

2 code implementations14 Oct 2020 Gabriele Moreno Berton, Valerio Paolicelli, Carlo Masone, Barbara Caputo

We address the task of cross-domain visual place recognition, where the goal is to geolocalize a given query image against a labeled gallery, in the case where the query and the gallery belong to different visual domains.

Unsupervised Domain Adaptation Visual Place Recognition

Shape Consistent 2D Keypoint Estimation under Domain Shift

no code implementations4 Aug 2020 Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini, Samuel Rota Bulo, Barbara Caputo, Elisa Ricci

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).

Depth Estimation Keypoint Estimation +2

Self-Supervised Learning Across Domains

no code implementations24 Jul 2020 Silvia Bucci, Antonio D'Innocente, Yujun Liao, Fabio Maria Carlucci, Barbara Caputo, Tatiana Tommasi

Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own.

Domain Generalization Object Recognition +2

Towards Recognizing Unseen Categories in Unseen Domains

1 code implementation ECCV 2020 Massimiliano Mancini, Zeynep Akata, Elisa Ricci, Barbara Caputo

The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training.

Domain Generalization Zero-Shot Learning +1

One-Shot Unsupervised Cross-Domain Detection

no code implementations ECCV 2020 Antonio D'Innocente, Francesco Cappio Borlino, Silvia Bucci, Barbara Caputo, Tatiana Tommasi

Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains.

object-detection Object Detection

Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition

3 code implementations21 Apr 2020 Mohammad Reza Loghmani, Luca Robbiano, Mirco Planamente, Kiru Park, Barbara Caputo, Markus Vincze

Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions.

Object Categorization Object Recognition +1

Boosting Deep Open World Recognition by Clustering

no code implementations20 Apr 2020 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set.

Incremental Learning Open Set Learning

IDDA: a large-scale multi-domain dataset for autonomous driving

no code implementations17 Apr 2020 Emanuele Alberti, Antonio Tavera, Carlo Masone, Barbara Caputo

To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains.

Autonomous Driving Domain Adaptation +1

Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

no code implementations10 Feb 2020 Mirco Planamente, Andrea Bottino, Barbara Caputo

Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view.

Action Recognition motion prediction +2

Modeling the Background for Incremental Learning in Semantic Segmentation

1 code implementation CVPR 2020 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i. e. pixels that do not belong to any other classes) exhibit a semantic distribution shift.

Continual Learning Disjoint 10-1 +8

Learning to Generalize One Sample at a Time with Self-Supervision

no code implementations9 Oct 2019 Antonio D'Innocente, Silvia Bucci, Barbara Caputo, Tatiana Tommasi

Although deep networks have significantly increased the performance of visual recognition methods, it is still challenging to achieve the robustness across visual domains that is necessary for real-world applications.

Auxiliary Learning Domain Generalization +1

Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition

no code implementations4 Jun 2019 Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo

While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize.

Open Set Learning

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

1 code implementation1 Apr 2019 Fabio Cermelli, Massimiliano Mancini, Elisa Ricci, Barbara Caputo

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others.

object-detection Object Detection +2

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

1 code implementation CVPR 2019 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines.

Domain Adaptation

Domain Generalization by Solving Jigsaw Puzzles

2 code implementations16 Mar 2019 Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, Tatiana Tommasi

Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own.

Domain Generalization Image Classification +1

Hallucinating Agnostic Images to Generalize Across Domains

1 code implementation3 Aug 2018 Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi, Barbara Caputo

The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems.

Domain Generalization Unsupervised Domain Adaptation

A recurrent multi-scale approach to RBG-D Object Recognition

no code implementations31 Jul 2018 Mirco Planamente, Mohammad Reza Loghmani, Barbara Caputo

Technological development aims to produce generations of increasingly efficient robots able to perform complex tasks.

Object Recognition

Multimodal Deep Domain Adaptation

no code implementations31 Jul 2018 Silvia Bucci, Mohammad Reza Loghmani, Barbara Caputo

Evaluations have been done using different data types: RGB only, depth only and RGB-D over the following datasets, designed for the robotic community: RGB-D Object Dataset (ROD), Web Object Dataset (WOD), Autonomous Robot Indoor Dataset (ARID), Big Berkeley Instance Recognition Dataset (BigBIRD) and Active Vision Dataset.

Domain Adaptation

Kitting in the Wild through Online Domain Adaptation

no code implementations3 Jul 2018 Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo

This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified.

Domain Adaptation Object Recognition

Best sources forward: domain generalization through source-specific nets

no code implementations15 Jun 2018 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions.

Domain Generalization Object Categorization

Recurrent Convolutional Fusion for RGB-D Object Recognition

1 code implementation5 Jun 2018 Mohammad Reza Loghmani, Mirco Planamente, Barbara Caputo, Markus Vincze

Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision.

Object Categorization Object Recognition

Robust Place Categorization with Deep Domain Generalization

1 code implementation30 May 2018 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Our method develops from the intuition that, given a set of different classification models associated to known domains (e. g. corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models.

Domain Generalization General Classification

Boosting Domain Adaptation by Discovering Latent Domains

2 code implementations CVPR 2018 Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution.

Domain Adaptation

Adaptive Deep Learning through Visual Domain Localization

1 code implementation24 Feb 2018 Gabriele Angeletti, Barbara Caputo, Tatiana Tommasi

We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture.

Domain Adaptation

Recognizing Objects In-the-wild: Where Do We Stand?

no code implementations18 Sep 2017 Mohammad Reza Loghmani, Barbara Caputo, Markus Vincze

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments.

Object Recognition

Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses

no code implementations29 Aug 2017 Andrea Gigli, Arjan Gijsberts, Valentina Gregori, Matteo Cognolato, Manfredo Atzori, Barbara Caputo

In this paper, we develop an automated way to detect stable fixations and show that gaze information is indeed helpful in predicting hand movements.

General Classification

From source to target and back: symmetric bi-directional adaptive GAN

no code implementations CVPR 2018 Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi, Barbara Caputo

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.

Image Generation Unsupervised Domain Adaptation

AutoDIAL: Automatic DomaIn Alignment Layers

1 code implementation ICCV 2017 Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò

Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one.

Domain Adaptation

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

1 code implementation IEEE Xplore: 2017 Nizar Massouh, Francesca Babiloni, Tatiana Tommasi, Jay Young, Nick Hawes, Barbara Caputo

We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.

Natural Language Processing Object Categorization +1

Adaptive Learning to Speed-Up Control of Prosthetic Hands: a Few Things Everybody Should Know

no code implementations27 Feb 2017 Valentina Gregori, Arjan Gijsberts, Barbara Caputo

A number of studies have proposed to use domain adaptation to reduce the training efforts needed to control an upper-limb prosthesis exploiting pre-trained models from prior subjects.

Domain Adaptation

Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

no code implementations21 Feb 2017 Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò

The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.

Unsupervised Domain Adaptation

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

no code implementations11 Jan 2017 Igor Barros Barbosa, Marco Cristani, Barbara Caputo, Aleksander Rognhaugen, Theoharis Theoharis

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire.

A deep representation for depth images from synthetic data

no code implementations30 Sep 2016 Fabio Maria Carlucci, Paolo Russo, Barbara Caputo

We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets.

Colorization Object Categorization

Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees

no code implementations26 Aug 2016 Valentina Gregori, Barbara Caputo

So-called domain adaptation algorithms formalize this strategy and have indeed been shown to significantly reduce the amount of required training data for intact subjects for myoelectric movements classification.

Domain Adaptation

Learning the Roots of Visual Domain Shift

no code implementations20 Jul 2016 Tatiana Tommasi, Martina Lanzi, Paolo Russo, Barbara Caputo

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set.

Domain Adaptation General Classification

When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks

no code implementations CVPR 2016 Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community.

Domain Adaptation

Active Learning for Online Recognition of Human Activities from Streaming Videos

no code implementations11 Apr 2016 Rocco De Rosa, Ilaria Gori, Fabio Cuzzolin, Barbara Caputo, Nicolò Cesa-Bianchi

Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long.

Active Learning

Online Open World Recognition

no code implementations8 Apr 2016 Rocco De Rosa, Thomas Mensink, Barbara Caputo

Recent attempts, like the open world recognition framework, tried to inject dynamics into the system by detecting new unknown classes and adding them incrementally, while at the same time continuously updating the models for the known classes.

Incremental Learning Metric Learning +1

When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks

no code implementations12 Nov 2015 Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community.

Domain Adaptation

A Deeper Look at Dataset Bias

no code implementations6 May 2015 Tatiana Tommasi, Novi Patricia, Barbara Caputo, Tinne Tuytelaars

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset.

Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation

no code implementations26 Mar 2015 Faraz Saeedan, Barbara Caputo

Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow.

Domain Adaptation Object Categorization

Scalable Greedy Algorithms for Transfer Learning

no code implementations6 Aug 2014 Ilja Kuzborskij, Francesco Orabona, Barbara Caputo

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task.

feature selection Transfer Learning

Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective

no code implementations CVPR 2014 Novi Patricia, Barbara Caputo

The transfer learning and domain adaptation problems originate from a distribution mismatch between the source and target data distribution.

Domain Adaptation

A Testbed for Cross-Dataset Analysis

no code implementations24 Feb 2014 Tatiana Tommasi, Tinne Tuytelaars, Barbara Caputo

Since its beginning visual recognition research has tried to capture the huge variability of the visual world in several image collections.

From N to N+1: Multiclass Transfer Incremental Learning

no code implementations CVPR 2013 Ilja Kuzborskij, Francesco Orabona, Barbara Caputo

Since the seminal work of Thrun [17], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks.

Incremental Learning Object Categorization +1

Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation

no code implementations NeurIPS 2009 Jie Luo, Barbara Caputo, Vittorio Ferrari

Given a corpus of news items consisting of images accompanied by text captions, we want to find out ``whos doing what, i. e. associate names and action verbs in the captions to the face and body pose of the persons in the images.

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