Few-Shot Semantic Segmentation
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Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION IMAGE REGISTRATION ZERO SHOT SEGMENTATION
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
• arnab39/FewShot_GAN-Unet3D
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In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.
3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION SEMI-SUPERVISED SEMANTIC SEGMENTATION
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
• HKUSTCV/FSS-1000
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In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only.
Adaptive Masked Proxies for Few-Shot Segmentation
• MSiam/AdaptiveMaskedProxies
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Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation.
FEW-SHOT SEMANTIC SEGMENTATION SEMANTIC SEGMENTATION VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION