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Found 13 papers, 6 papers with code
Training Semantic Segmentation on Heterogeneous Datasets
We explore semantic segmentation beyond the conventional, single-dataset homogeneous training and bring forward the problem of Heterogeneous Training of Semantic Segmentation (HTSS).
Towards holistic scene understanding: Semantic segmentation and beyond
Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework.
Deep Adaptive Multi-Intention Inverse Reinforcement Learning
This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn an a priori unknown number of nonlinear reward functions from unlabeled experts' demonstrations.
Merging Tasks for Video Panoptic Segmentation
In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed.
Part-aware Panoptic Segmentation
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing.
Ranked #2 on
Image Segmentation
on Pascal Panoptic Parts
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
In this technical report, we present two novel datasets for image scene understanding.
Fast Panoptic Segmentation Network
For lower resolutions of the Cityscapes dataset and for the Pascal VOC dataset, FPSNet runs at 22 and 35 frames per second, respectively.
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data.
On Boosting Semantic Street Scene Segmentation with Weak Supervision
We collect street scene images and weak labels from the immense Open Images dataset to generate the OpenScapes dataset, and we use this novel dataset to increase segmentation performance on two established per-pixel labeled datasets, Cityscapes and Vistas.
Single Network Panoptic Segmentation for Street Scene Understanding
Our network is evaluated on two street scene datasets: Cityscapes and Mapillary Vistas.
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled datasets and the focus of our research.
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network
For instance segmentation, a Mask R-CNN type of architecture is used, while the semantic segmentation branch is augmented with a Pyramid Pooling Module.
Ranked #11 on
Panoptic Segmentation
on Mapillary val
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
We propose a convolutional network with hierarchical classifiers for per-pixel semantic segmentation, which is able to be trained on multiple, heterogeneous datasets and exploit their semantic hierarchy.
Ranked #5 on
Semantic Segmentation
on KITTI Semantic Segmentation
