47 citations · 110 across the 27 of their papers we have counts for
31 papers · 1 filter
An Outlook into the Future of Egocentric Vision
Chiara Plizzari, Gabriele Goletto, Antonino Furnari +5
What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with…
Large Class Separation is not what you need for Relational Reasoning-based OOD Detection
Lorenzo Li Lu, Giulia D'Ascenzi, Francesco Cappio Borlino +1
Standard recognition approaches are unable to deal with novel categories at test time. Their overconfidence on the known classes makes the predictions unreliable for safety-critica…
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales +1
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when maki…
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi
In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies fo…
Semantic Novelty Detection via Relational Reasoning
Francesco Cappio Borlino, Silvia Bucci, Tatiana Tommasi
Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving o…
Contrastive Learning for Cross-Domain Open World Recognition
Francesco Cappio Borlino, Silvia Bucci, Tatiana Tommasi
The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer. Consider for example a home assis…
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation
Silvia Bucci, Francesco Cappio Borlino, Barbara Caputo +1
Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move to…
Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
F. Cappio Borlino, S. Polizzotto, B. Caputo +1
Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models are used off-the-shelf on unseen domains. Most of the existing works…
Denoise and Contrast for Category Agnostic Shape Completion
Antonio Alliegro, Diego Valsesia, Giulia Fracastoro +2
In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region a…
Multi-Modal RGB-D Scene Recognition Across Domains
Andrea Ferreri, Silvia Bucci, Tatiana Tommasi
Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that…
Rethinking Domain Generalization Baselines
Francesco Cappio Borlino, Antonio D'Innocente, Tatiana Tommasi
Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained.…
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Silvia Bucci, Mohammad Reza Loghmani, Tatiana Tommasi
Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in…
Self-Supervised Learning Across Domains
Silvia Bucci, Antonio D'Innocente, Yujun Liao +3
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 childr…
One-Shot Unsupervised Cross-Domain Detection
Antonio D'Innocente, Francesco Cappio Borlino, Silvia Bucci +2
Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains. Although the topic has attracte…
Bridging the gap between Natural and Medical Images through Deep Colorization
Lia Morra, Luca Piano, Fabrizio Lamberti +1
Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive du…
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Antonio Alliegro, Davide Boscaini, Tatiana Tommasi
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are ess…
Learning to Generalize One Sample at a Time with Self-Supervision
Antonio D'Innocente, Silvia Bucci, Barbara Caputo +1
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 n…
Tackling Partial Domain Adaptation with Self-Supervision
Silvia Bucci, Antonio D'Innocente, Tatiana Tommasi
Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains. They allow to train reliable models that work over…
Domain Generalization by Solving Jigsaw Puzzles
Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci +2
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 th…
Hallucinating Agnostic Images to Generalize Across Domains
Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi +1
The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, t…
Adaptive Deep Learning through Visual Domain Localization
Gabriele Angeletti, Barbara Caputo, Tatiana Tommasi
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illuminat…
From source to target and back: symmetric bi-directional adaptive GAN
Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi +1
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 unsupervis…
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Nizar Massouh, Francesca Babiloni, Tatiana Tommasi +3
Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification.…
Combining Multiple Cues for Visual Madlibs Question Answering
Tatiana Tommasi, Arun Mallya, Bryan Plummer +3
This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset. Instead of generic and commonly used representations trai…