5 citations · 9 across the 7 of their papers we have counts for
9 papers
OpenPatch: a 3D patchwork for Out-Of-Distribution detection
Paolo Rabino, Antonio Alliegro, Francesco Cappio Borlino +1
Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel cl…
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…
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…
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.…
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…