Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SynthRef: Generation of Synthetic Referring Expressions for Object
  Segmentation   Recent advances in deep learning have brought significant progress in visual
grounding tasks such as language-guided video object segmentation. However,
collecting large datasets for these tasks is expensive in terms of annotation
time, which represents a bottleneck. To this end, we propose a novel method,
namely SynthRef, for generating synthetic referring expressions for target
objects in an image (or video frame), and we also present and disseminate the
first large-scale dataset with synthetic referring expressions for video object
segmentation. Our experiments demonstrate that by training with our synthetic
referring expressions one can improve the ability of a model to generalize
across different datasets, without any additional annotation cost. Moreover,
our formulation allows its application to any object detection or segmentation
dataset.

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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci