Search Results for author:
Found 16 papers, 11 papers with code
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control
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.
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
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.
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
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)
SILC: Improving Vision Language Pretraining with Self-Distillation
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.
LiDAR Meta Depth Completion
While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns.
EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation
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
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation
As previous UDA&DG semantic segmentation methods are mostly based on outdated networks, we benchmark more recent architectures, reveal the potential of Transformers, and design the DAFormer network tailored for UDA&DG.
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
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.
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
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
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
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
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
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.
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
However, such a supervision is not always available.
Ranked #15 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
(using extra training data)
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
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)
Grid Saliency for Context Explanations of Semantic Segmentation
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions.
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
An environment representation (ER) is a substantial part of every autonomous system.
A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
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.
