34 citations · 65 across the 22 of their papers we have counts for
24 papers
Robust Promptable Video Object Segmentation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +3
The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in safety-critical domains. Thi…
Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentation
Jonas Ernst, Wolfgang Boettcher, Lukas Hoyer +2
We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obta…
S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation
Leon Sick, Lukas Hoyer, Dominik Engel +2
In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as I…
RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving
Arpit Jadon, Haoran Wang, Phillip Thomas +7
As perception models continue to develop, the need for large-scale datasets increases. However, data annotation remains far too expensive to effectively scale and meet the demand.…
GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +1
Improving robustness of the Segment Anything Model (SAM) to input degradations is critical for its deployment in high-stakes applications such as autonomous driving and robotics. O…
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Klara Reichard, Giulia Rizzoli, Stefano Gasperini +4
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, cu…
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Linyan Yang, Lukas Hoyer, Mark Weber +6
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that c…
Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
Wolfgang Boettcher, Lukas Hoyer, Ozan Unal +2
In this work, we introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. Training or fine-tuning semantic…
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…
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Lukas Hoyer, Dengxin Dai, Haoran Wang +1
In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previ…
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further…
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapt…
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Qin Wang +2
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Qin Wang, Dengxin Dai, Lukas Hoyer +2
Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervisi…
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Yuhua Chen +3
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
Grid Saliency for Context Explanations of Semantic Segmentation
Lukas Hoyer, Mauricio Munoz, Prateek Katiyar +2
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limi…
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
Lukas Hoyer, Patrick Kesper, Anna Khoreva +1
An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decisi…
A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
Lukas Hoyer, Christoph Steup, Sanaz Mostaghim
External localization is an essential part for the indoor operation of small or cost-efficient robots, as they are used, for example, in swarm robotics. We introduce a two-stage lo…