34 citations · 65 across the 22 of their papers we have counts for
7 papers · 1 filter
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control
Yuru Jia, Lukas Hoyer, Shengyu Huang +4
Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability to generate creative content, specialize to user data through few-shot fine-tuning, and…
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
Lukas Hoyer, David Joseph Tan, Muhammad Ferjad Naeem +2
In semi-supervised semantic segmentation, a model is trained with a limited number of labeled images along with a large corpus of unlabeled images to reduce the high annotation eff…
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
Ozan Unal, Dengxin Dai, Lukas Hoyer +2
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necess…
SILC: Improving Vision Language Pretraining with Self-Distillation
Muhammad Ferjad Naeem, Yongqin Xian, Xiaohua Zhai +3
Image-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and i…
LiDAR Meta Depth Completion
Wolfgang Boettcher, Lukas Hoyer, Ozan Unal +2
Depth estimation is one of the essential tasks to be addressed when creating mobile autonomous systems. While monocular depth estimation methods have improved in recent times, dept…
EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation
Suman Saha, Lukas Hoyer, Anton Obukhov +2
With autonomous industries on the rise, domain adaptation of the visual perception stack is an important research direction due to the cost savings promise. Much prior art was dedi…
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine learning models trained on a source domain to perform well on unlabeled or even unseen target dom…