activity
20152021
most citedInferring Latent Domains for Unsupervised Deep Domain Adaptation

36 citations · 48 across the 9 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.CV2020

Shape Consistent 2D Keypoint Estimation under Domain Shift

Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini +3

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex pr…

cs.CV20205 cited

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…

cs.CV2020

Boosting Deep Open World Recognition by Clustering

Dario Fontanel, Fabio Cermelli, Massimiliano Mancini +3

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concep…

cs.CV2020

Modeling the Background for Incremental Learning in Semantic Segmentation

Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò +2

Despite their effectiveness in a wide range of tasks, deep architectures suffer from some important limitations. In particular, they are vulnerable to catastrophic forgetting, i.e.…

cs.CV2020

Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

Mirco Planamente, Andrea Bottino, Barbara Caputo

Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from…