Add to ORKGTraining semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS-Seg), to analyze the generalization ability of simultaneously segmenting the novel categories with very few examples and the base categories with sufficient examples. It is the first study showing that previous representative state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained setting of FS-Seg. To make GFS-Seg tractable, we set up a GFS...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referr...
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-s...
This paper introduces a generalized few-shot segmentation framework with a straightforward training ...
Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentati...
Semantic segmentation performs pixel-wise classification for given images, which can be widely used ...
Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few anno...
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only ...
Semantic segmentation models have two fundamental weaknesses: i) they require large training sets wi...
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot l...
Few-shot segmentation aims to devise a generalizing model that segments query images from unseen cla...
Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled sampl...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Fully-supervised deep learning segmentation models are inflexible when encountering new unseen seman...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referr...
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-s...
This paper introduces a generalized few-shot segmentation framework with a straightforward training ...
Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentati...
Semantic segmentation performs pixel-wise classification for given images, which can be widely used ...
Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few anno...
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only ...
Semantic segmentation models have two fundamental weaknesses: i) they require large training sets wi...
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot l...
Few-shot segmentation aims to devise a generalizing model that segments query images from unseen cla...
Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled sampl...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Fully-supervised deep learning segmentation models are inflexible when encountering new unseen seman...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referr...
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-s...