49 citations · 217 across the 32 of their papers we have counts for
6 papers · 1 filter
Towards Recognizing New Semantic Concepts in New Visual Domains
Massimiliano Mancini
Deep learning models heavily rely on large scale annotated datasets for training. Unfortunately, datasets cannot capture the infinite variability of the real world, thus neural net…
Prototype-based Incremental Few-Shot Semantic Segmentation
Fabio Cermelli, Massimiliano Mancini, Yongqin Xian +2
Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, cons…
Towards Recognizing Unseen Categories in Unseen Domains
Massimiliano Mancini, Zeynep Akata, Elisa Ricci +1
Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core…
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…
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…
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.…