36 citations · 48 across the 9 of their papers we have counts for
11 papers · 1 filter
Domain Generalization with Domain-Specific Aggregation Modules
Antonio D'Innocente, Barbara Caputo
Visual recognition systems are meant to work in the real world. For this to happen, they must work robustly in any visual domain, and not only on the data used during training. Wit…
A recurrent multi-scale approach to RBG-D Object Recognition
Mirco Planamente, Mohammad Reza Loghmani, Barbara Caputo
Technological development aims to produce generations of increasingly efficient robots able to perform complex tasks. This requires considerable efforts, from the scientific commun…
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…
Multimodal Deep Domain Adaptation
Silvia Bucci, Mohammad Reza Loghmani, Barbara Caputo
Typically a classifier trained on a given dataset (source domain) does not performs well if it is tested on data acquired in a different setting (target domain). This is the proble…
Kitting in the Wild through Online Domain Adaptation
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci +2
Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working…
Best sources forward: domain generalization through source-specific nets
Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo +1
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covaria…