Search Results for author:
Found 9 papers, 6 papers with code
Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation
Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of works in other tasks {such} as segmentation and detection.
FuSS: Fusing Superpixels for Improved Segmentation Consistency
In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation.
Conditional Reconstruction for Open-set Semantic Segmentation
Open set segmentation is a relatively new and unexploredtask, with just a handful of methods proposed to model suchtasks. We propose a novel method called CoReSeg thattackles the issue using class conditional reconstruction ofthe input images according to their pixelwise mask.
Weakly Supervised Few-Shot Segmentation Via Meta-Learning
Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis.
Learning to Segment Medical Images from Few-Shot Sparse Labels
In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images.
Few-Shot Semantic Segmentation
Medical Image Segmentation
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Opening Deep Neural Networks with Generative Models
Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes.
AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification
Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery.
Fully Convolutional Open Set Segmentation
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches.
Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation
We merge these unsupervised networks with supervised deep semantic segmentation architectures in order to create a semi-supervised method capable of learning from both unlabeled and labeled data, whenever labeling is available.
