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BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation
In this work, we define and address "Boundless Unsupervised Domain Adapt...
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Zero-Shot Semantic Segmentation
Semantic segmentation models are limited in their ability to scale to la...
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DADA: Depth-aware Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) is important for applications where...
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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Semantic segmentation is a key problem for many computer vision tasks. W...
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Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images th...
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Generating Visual Representations for Zero-Shot Classification
This paper addresses the task of learning an image clas-sifier when some...
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Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Zero-Shot learning has been shown to be an efficient strategy for domain...
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Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key...
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