Manuel Schwonberg
YOU?
Author Swipe
Domain Generalization for Semantic Segmentation: A Survey Open
The generalization of deep neural networks to unknown domains is a major challenge despite their tremendous progress in recent years. For this reason, the dynamic area of domain generalization (DG) has emerged. In contrast to unsupervised …
VLTSeg: A Domain Generalized Semantic Segmentation Model Open
The associated GitHub repository provides an unofficial reimplementation of VLTSeg, the semantic segmentation model from the paper Strong but Simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning, since…
A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models Open
Despite the recent progress in deep learning based computer vision, domain shifts are still one of the major challenges. Semantic segmentation for autonomous driving faces a wide range of domain shifts, e.g. caused by changing weather cond…
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation Open
When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods try to overcome this issue, but need samples from the targ…
Augmentation-based Domain Generalization for Semantic Segmentation Open
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unseen domains. While UDA methods have access to unlabeled targ…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving Open
Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play a significant role in environment perception for the challe…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving Open
Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semanti…