Search Results for author:
Found 7 papers, 3 papers with code
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.
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
Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images
Recent progress of deep image classification models has provided great potential to improve state-of-the-art performance in related computer vision tasks.
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.
A Novel Georeferenced Dataset for Stereo Visual Odometry
In this work, we present a novel dataset for assessing the accuracy of stereo visual odometry.
