1 citations · 1 across the 8 of their papers we have counts for
9 papers
GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure
Mohamed Abdelsamad, Bin Yang, Michael Ulrich +4
3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers wel…
Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds
Bin Yang, Mohamed Abdelsamad, Miao Zhang +1
Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Existing approaches emphasize seman…
DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representation
Mohamed Abdelsamad, Michael Ulrich, Bin Yang +3
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point c…
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Mohamed Abdelsamad, Michael Ulrich, Claudius Gläser +1
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are p…
A Semi-Paired Approach For Label-to-Image Translation
George Eskandar, Shuai Zhang, Mohamed Abdelsamad +3
Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition t…
Wavelet-based Unsupervised Label-to-Image Translation
George Eskandar, Mohamed Abdelsamad, Karim Armanious +2
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generati…
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
Karim Guirguis, Mohamed Abdelsamad, George Eskandar +4
Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learn…
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
Karim Guirguis, Ahmed Hendawy, George Eskandar +3
Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-…
USIS: Unsupervised Semantic Image Synthesis
George Eskandar, Mohamed Abdelsamad, Karim Armanious +1
Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask. SIS has mostly been addressed as a…