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most citedA Deeper Look at Dataset Bias

5 citations · 22 across the 17 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021

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…

cs.AI2021

Towards Fairness Certification in Artificial Intelligence

Tatiana Tommasi, Silvia Bucci, Barbara Caputo +1

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 labor…

cs.CV2021

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…

cs.CV2021

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…

cs.CV2021

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…

cs.CV2021

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.…