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Found 14 papers, 7 papers with code
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life.
WiCV 2022: The Tenth Women In Computer Vision Workshop
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana.
Semantic Novelty Detection via Relational Reasoning
We claim that a tailored representation learning strategy may be the right solution for effective and efficient semantic novelty detection.
Contrastive Learning for Cross-Domain Open World Recognition
The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer.
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation
Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time.
Towards Fairness Certification in Artificial Intelligence
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.
Multi-Modal RGB-D Scene Recognition Across Domains
Indeed, learning to go from RGB to depth and vice-versa is an unsupervised procedure that can be trained jointly on data of multiple cameras and may help to bridge the gap among the extracted feature distributions.
Self-Supervised Learning Across Domains
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.
Ranked #79 on
Domain Generalization
on PACS
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
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 the source.
One-Shot Unsupervised Cross-Domain Detection
Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains.
Learning to Generalize One Sample at a Time with Self-Supervision
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.
Tackling Partial Domain Adaptation with Self-Supervision
Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains.
Domain Generalization by Solving Jigsaw Puzzles
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.
Ranked #3 on
Domain Generalization
on NICO Animal
Multimodal Deep Domain Adaptation
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.
