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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.
Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing.
Multi-domain semantic segmentation with overlapping labels
Deep supervised models have an unprecedented capacity to absorb large quantities of training data.
Revisiting consistency for semi-supervised semantic segmentation
Our experiments show clear advantages of (1) one-way consistency, (2) perturbing only the student branch, and (3) strong photometric and geometric perturbations.
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.
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
Single Level Feature-to-Feature Forecasting with Deformable Convolutions
We present a method to anticipate semantic segmentation of future frames in driving scenarios based on feature-to-feature forecasting.
Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings
We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings.
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.
