29 citations · 158 across the 40 of their papers we have counts for
17 papers · 1 filter
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Edoardo Arnaudo, Fabio Cermelli, Antonio Tavera +2
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning cur…
E(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
Chiara Plizzari, Mirco Planamente, Gabriele Goletto +4
Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have…
Incremental Learning in Semantic Segmentation from Image Labels
Fabio Cermelli, Dario Fontanel, Antonio Tavera +2
Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore…
Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation
Antonio Tavera, Carlo Masone, Barbara Caputo
Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computation…
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
Antonio Tavera, Fabio Cermelli, Carlo Masone +1
In this paper we consider the task of semantic segmentation in autonomous driving applications. Specifically, we consider the cross-domain few-shot setting where training can use o…
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition
Mirco Planamente, Chiara Plizzari, Emanuele Alberti +1
First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that…
Viewpoint Invariant Dense Matching for Visual Geolocalization
Gabriele Berton, Carlo Masone, Valerio Paolicelli +1
In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against ch…
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…
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
Chiara Plizzari, Mirco Planamente, Emanuele Alberti +1
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the dom…
Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment
Mirco Planamente, Chiara Plizzari, Emanuele Alberti +1
First person action recognition is an increasingly researched topic because of the growing popularity of wearable cameras. This is bringing to light cross-domain issues that are ye…
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…
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…
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…
DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation
Mirco Planamente, Chiara Plizzari, Marco Cannici +5
Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". The innovative way they acquire data presents seve…
Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
Luca Robbiano, Muhammad Rameez Ur Rahman, Fabio Galasso +2
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date,…