Search Results for author:
Found 12 papers, 6 papers with code
Weakly supervised training of universal visual concepts for multi-domain semantic segmentation
For instance, pickups are labeled as trucks in VIPER, cars in Vistas, and vans in ADE20k.
Automatic universal taxonomies for multi-domain semantic segmentation
Training semantic segmentation models on multiple datasets has sparked a lot of recent interest in the computer vision community.
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
We blend these two predictions into a hybrid anomaly score which allows dense open-set recognition on large natural images.
Ranked #3 on
Scene Segmentation
on StreetHazards
(using extra training data)
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
Standard machine learning is unable to accommodate inputs which do not belong to the training distribution.
Ranked #2 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
Multi-domain semantic segmentation with overlapping labels
Deep supervised models have an unprecedented capacity to absorb large quantities of training data.
Dense outlier detection and open-set recognition based on training with noisy negative images
Deep convolutional models often produce inadequate predictions for inputs foreign to the training distribution.
Dense open-set recognition with synthetic outliers generated by Real NVP
We obtain the synthetic outliers by sampling an RNVP model which is jointly trained to generate datapoints at the border of the training distribution.
Multi-domain semantic segmentation with pyramidal fusion
We present our submission to the semantic segmentation contest of the Robust Vision Challenge held at ECCV 2020.
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
Recent success on realistic road driving datasets has increased interest in exploring robust performance in real-world applications.
Ranked #14 on
Anomaly Detection
on Fishyscapes L&F
In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images
Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields.
Ranked #9 on
Semantic Segmentation
on ZJU-RGB-P
Discriminative out-of-distribution detection for semantic segmentation
Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes.
Robust Semantic Segmentation with Ladder-DenseNet Models
We present semantic segmentation experiments with a model capable to perform predictions on four benchmark datasets: Cityscapes, ScanNet, WildDash and KITTI.
