Search Results for author:
Found 12 papers, 9 papers with code
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.
Incremental learning for the detection and classification of GAN-generated images
Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones.
Low-Budget Label Query through Domain Alignment Enforcement
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds.
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
Novel Class Discovery for 3D Point Cloud Semantic Segmentation
Firstly, we address the new problem of NCD for point cloud semantic segmentation.
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
To answer this question, we design the first experimental setup for studying domain generalization (DG) for LiDAR semantic segmentation (DG-LSS).
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.
Novel class discovery meets foundation models for 3D semantic segmentation
Firstly, it introduces the novel task of NCD for point cloud semantic segmentation.
