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One-shot learning

One-shot learning is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object… Expand
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Papers overview

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Highly Cited
2018
Highly Cited
2018
We focus on the one-shot learning for video-based person re-Identification (re-ID). Unlabeled tracklets for the person re-ID… Expand
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Highly Cited
2017
Highly Cited
2017
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background… Expand
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Highly Cited
2017
Highly Cited
2017
Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive… Expand
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Highly Cited
2017
Highly Cited
2017
Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense… Expand
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Highly Cited
2016
Highly Cited
2016
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as… Expand
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Highly Cited
2016
Highly Cited
2016
Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is… Expand
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Highly Cited
2016
Highly Cited
2016
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on… Expand
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Highly Cited
2015
Highly Cited
2015
  • Gregory R. Koch
  • 2015
  • Corpus ID: 13874643
The process of learning good features for machine learning applications can be very computationally expensive and may prove… Expand
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2015
2015
The task of discriminating one object from another is almost trivial for a human being. However, this task is computationally… Expand
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Highly Cited
2003
Highly Cited
2003
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and… Expand
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