Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highl...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
Semantic segmentation is an important but challenging task in computer vision because it aims to ass...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
The purpose of few-shot semantic segmentation is to segment unseen classes with only a few labeled s...
Contextual information and the dependencies between dimensions is vital in image semantic segmentati...
Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled sampl...
Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in ...
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding feature...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
Semantic segmentation is an important but challenging task in computer vision because it aims to ass...
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning ...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
International audienceDeep learning-based image understanding techniques require a large number of l...
The purpose of few-shot semantic segmentation is to segment unseen classes with only a few labeled s...
Contextual information and the dependencies between dimensions is vital in image semantic segmentati...
Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled sampl...
Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in ...
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding feature...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a f...
Semantic segmentation is an important but challenging task in computer vision because it aims to ass...