49 citations · 217 across the 32 of their papers we have counts for
9 papers · 1 filter
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning
Anjan Dutta, Massimiliano Mancini, Zeynep Akata
Existing self-supervised learning methods learn representation by means of pretext tasks which are either (1) discriminating that explicitly specify which features should be separa…
On the Challenges of Open World Recognitionunder Shifting Visual Domains
Dario Fontanel, Fabio Cermelli, Massimiliano Mancini +1
Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including un…
Detecting Anomalies in Semantic Segmentation with Prototypes
Dario Fontanel, Fabio Cermelli, Massimiliano Mancini +1
Traditional semantic segmentation methods can recognize at test time only the classes that are present in the training set. This is a significant limitation, especially for semanti…
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian +1
Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assump…
Cluster-driven Graph Federated Learning over Multiple Domains
Debora Caldarola, Massimiliano Mancini, Fabio Galasso +3
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The ce…
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Giuseppe Pastore, Fabio Cermelli, Yongqin Xian +3
Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation…
Boosting Binary Masks for Multi-Domain Learning through Affine Transformations
Massimiliano Mancini, Elisa Ricci, Barbara Caputo +1
In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain lea…
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò +2
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data…
Open World Compositional Zero-Shot Learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian +1
Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen c…