Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixed Supervised Object Detection by Transferring Mask Prior and
  Semantic Similarity   Object detection has achieved promising success, but requires large-scale
fully-annotated data, which is time-consuming and labor-extensive. Therefore,
we consider object detection with mixed supervision, which learns novel object
categories using weak annotations with the help of full annotations of existing
base object categories. Previous works using mixed supervision mainly learn the
class-agnostic objectness from fully-annotated categories, which can be
transferred to upgrade the weak annotations to pseudo full annotations for
novel categories. In this paper, we further transfer mask prior and semantic
similarity to bridge the gap between novel categories and base categories.
Specifically, the ability of using mask prior to help detect objects is learned
from base categories and transferred to novel categories. Moreover, the
semantic similarity between objects learned from base categories is transferred
to denoise the pseudo full annotations for novel categories. Experimental
results on three benchmark datasets demonstrate the effectiveness of our method
over existing methods. Codes are available at
https://github.com/bcmi/TraMaS-Weak-Shot-Object-Detection.

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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int