18 citations · 36 across the 8 of their papers we have counts for
17 papers
Query-Guided Networks for Few-shot Fine-grained Classification and Person Search
Bharti Munjal, Alessandro Flaborea, Sikandar Amin +2
Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both t…
Under the Hood of Transformer Networks for Trajectory Forecasting
Luca Franco, Leonardo Placidi, Francesco Giuliari +3
Transformer Networks have established themselves as the de-facto state-of-the-art for trajectory forecasting but there is currently no systematic study on their capability to model…
Space-Time-Separable Graph Convolutional Network for Pose Forecasting
Theodoros Sofianos, Alessio Sampieri, Luca Franco +1
Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications. Research has main…
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…
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,…
SF-UDA: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe +2
3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern street-view benchmarks. However, LiDAR-based detectors poorly generalize across domains due…