103 citations · 309 across the 21 of their papers we have counts for
11 papers · 1 filter
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?
Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi +2
Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark,…
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
Pierfrancesco Ardino, Yahui Liu, Elisa Ricci +2
Manipulating images of complex scenes to reconstruct, insert and/or remove specific object instances is a challenging task. Complex scenes contain multiple semantics and objects, w…
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…
Learning to Cluster under Domain Shift
Willi Menapace, Stéphane Lathuilière, Elisa Ricci
While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i.e. labe…
Towards Recognizing Unseen Categories in Unseen Domains
Massimiliano Mancini, Zeynep Akata, Elisa Ricci +1
Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core…
Shape Consistent 2D Keypoint Estimation under Domain Shift
Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini +3
Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex pr…