1 citations · 2 across the 8 of their papers we have counts for
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MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Elena Camuffo, Francesco Barbato, Mete Ozay +2
Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightweight models often struggle in this sett…
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics
Elena Camuffo, Umberto Michieli, Simone Milani +2
Developing a reliable vision system is a fundamental challenge for robotic technologies (e.g., indoor service robots and outdoor autonomous robots) which can ensure reliable naviga…
FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
Elena Camuffo, Umberto Michieli, Jijoong Moon +2
Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust te…
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network
Francesco Barbato, Elena Camuffo, Simone Milani +1
State-of-the-art multimodal semantic segmentation strategies combining LiDAR and color data are usually designed on top of asymmetric information-sharing schemes and assume that bo…
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data
Elena Camuffo, Simone Milani
During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catas…
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation
Elena Camuffo, Umberto Michieli, Simone Milani
Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accompl…