Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned
  from Synthetic Data   We present a network architecture which compares RGB images and untextured 3D
models by the similarity of the represented shape. Our system is optimised for
zero-shot retrieval, meaning it can recognise shapes never shown in training.
We use a view-based shape descriptor and a siamese network to learn object
geometry from pairs of 3D models and 2D images. Due to scarcity of datasets
with exact photograph-mesh correspondences, we train our network with only
synthetic data. Our experiments investigate the effect of different qualities
and quantities of training data on retrieval accuracy and present insights from
bridging the domain gap. We show that increasing the variety of synthetic data
improves retrieval accuracy and that our system's performance in zero-shot mode
can match that of the instance-aware mode, as far as narrowing down the search
to the top 10% of objects.

16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc