Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Zero-Shot Learning for Semantic Segmentation of 3D Point
  Cloud   While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D
images, its application to 3D data is still recent and scarce, with just a few
methods limited to classification. We present the first generative approach for
both ZSL and Generalized ZSL (GZSL) on 3D data, that can handle both
classification and, for the first time, semantic segmentation. We show that it
reaches or outperforms the state of the art on ModelNet40 classification for
both inductive ZSL and inductive GZSL. For semantic segmentation, we created
three benchmarks for evaluating this new ZSL task, using S3DIS, ScanNet and
SemanticKITTI. Our experiments show that our method outperforms strong
baselines, which we additionally propose for this task.

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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas