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Found 11 papers, 6 papers with code
ShelfNet for Fast Semantic Segmentation
Compared with real-time segmentation models such as BiSeNet, our model achieves higher accuracy at comparable speed on the Cityscapes Dataset, enabling the application in speed-demanding tasks such as street-scene understanding for autonomous driving.
Ranked #14 on
Semantic Segmentation
on PASCAL VOC 2012 test
Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
Our work significantly improves the snippet-context baseline F1 score on GAP dataset from 66. 9% to 80. 3%.
Robust and Generalizable Visual Representation Learning via Random Convolutions
In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation.
Ranked #110 on
Domain Generalization
on PACS
Decision Explanation and Feature Importance for Invertible Networks
We can determine the decision boundary of a linear classifier in the feature space; since the transform is invertible, we can invert the decision boundary from the feature space to the input space.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space.
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
For the DA task, our DALACE model outperformed CycleGAN, TD-GAN , and DADR with DSC of 0. 847 compared to 0. 721, 0. 793 and 0. 806.
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making.
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation
However, the well-trained models often fail in the target domain due to the domain shift.
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts.
