no code implementations • 5 Dec 2023 • Yuru Jia, Lukas Hoyer, Shengyu Huang, Tianfu Wang, Luc van Gool, Konrad Schindler, Anton Obukhov
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 condition their output on other modalities, such as semantic maps.
no code implementations • 27 Nov 2023 • Ozan Unal, Dengxin Dai, Lukas Hoyer, Yigit Baran Can, Luc van Gool
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 necessity for dense annotations by employing weakly-supervised training.
Ranked #1 on 3D Semantic Segmentation on ScribbleKITTI
1 code implementation • 27 Nov 2023 • Lukas Hoyer, David Joseph Tan, Muhammad Ferjad Naeem, Luc van Gool, Federico Tombari
In SemiVL, we propose to integrate rich priors from VLM pre-training into semi-supervised semantic segmentation to learn better semantic decision boundaries.
Ranked #1 on Semi-Supervised Semantic Segmentation on PASCAL VOC 2012 732 labeled (using extra training data)
no code implementations • 20 Oct 2023 • Muhammad Ferjad Naeem, Yongqin Xian, Xiaohua Zhai, Lukas Hoyer, Luc van Gool, Federico Tombari
However, the contrastive objective used by these models only focuses on image-text alignment and does not incentivise image feature learning for dense prediction tasks.
1 code implementation • 24 Jul 2023 • Wolfgang Boettcher, Lukas Hoyer, Ozan Unal, Ke Li, Dengxin Dai
While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns.
1 code implementation • ICCV 2023 • Suman Saha, Lukas Hoyer, Anton Obukhov, Dengxin Dai, Luc van Gool
EDAPS significantly improves the state-of-the-art performance for panoptic segmentation UDA by a large margin of 20% on SYNTHIA-to-Cityscapes and even 72% on the more challenging SYNTHIA-to-Mapillary Vistas.
Ranked #1 on Domain Adaptation on Panoptic SYNTHIA-to-Mapillary
3 code implementations • 26 Apr 2023 • Lukas Hoyer, Dengxin Dai, Luc van Gool
As previous UDA&DG semantic segmentation methods are mostly based on outdated networks, we benchmark more recent architectures, reveal the potential of Transformers, and design the DAFormer network tailored for UDA&DG.
1 code implementation • CVPR 2023 • Lukas Hoyer, Dengxin Dai, Haoran Wang, Luc van Gool
MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA.
1 code implementation • 27 Apr 2022 • Lukas Hoyer, Dengxin Dai, Luc van Gool
Therefore, we propose HRDA, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to preserve fine segmentation details and large low-resolution crops to capture long-range context dependencies with a learned scale attention, while maintaining a manageable GPU memory footprint.
Ranked #3 on Semantic Segmentation on GTAV-to-Cityscapes Labels
3 code implementations • CVPR 2022 • Lukas Hoyer, Dengxin Dai, Luc van Gool
It improves the state of the art by 10. 8 mIoU for GTA-to-Cityscapes and 5. 4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes such as train, bus, and truck well.
Ranked #5 on Semantic Segmentation on DensePASS
1 code implementation • 28 Aug 2021 • Lukas Hoyer, Dengxin Dai, Qin Wang, Yuhua Chen, Luc van Gool
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 highly labor-intensive process.
1 code implementation • ICCV 2021 • Qin Wang, Dengxin Dai, Lukas Hoyer, Luc van Gool, Olga Fink
However, such a supervision is not always available.
Ranked #15 on Domain Adaptation on SYNTHIA-to-Cityscapes (using extra training data)
1 code implementation • CVPR 2021 • Lukas Hoyer, Dengxin Dai, Yuhua Chen, Adrian Köring, Suman Saha, Luc van Gool
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 highly labor-intensive process.
Ranked #4 on Semi-Supervised Semantic Segmentation on Cityscapes 100 samples labeled (using extra training data)
2 code implementations • NeurIPS 2019 • Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions.
no code implementations • 21 Mar 2019 • Lukas Hoyer, Patrick Kesper, Anna Khoreva, Volker Fischer
An environment representation (ER) is a substantial part of every autonomous system.
no code implementations • 3 Oct 2018 • Lukas Hoyer, Christoph Steup, Sanaz Mostaghim
Object detection is performed on an external camera image of the operation zone providing robot bounding boxes for an identification and orientation estimation convolutional neural network.