Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Scene-specific Object Detectors Based on a
  Generative-Discriminative Model with Minimal Supervision   One object class may show large variations due to diverse illuminations,
backgrounds and camera viewpoints. Traditional object detection methods often
perform worse under unconstrained video environments. To address this problem,
many modern approaches model deep hierarchical appearance representations for
object detection. Most of these methods require a timeconsuming training
process on large manual labelling sample set. In this paper, the proposed
framework takes a remarkably different direction to resolve the multi-scene
detection problem in a bottom-up fashion. First, a scene-specific objector is
obtained from a fully autonomous learning process triggered by marking several
bounding boxes around the object in the first video frame via a mouse. Here the
human labeled training data or a generic detector are not needed. Second, this
learning process is conveniently replicated many times in different
surveillance scenes and results in particular detectors under various camera
viewpoints. Thus, the proposed framework can be employed in multi-scene object
detection applications with minimal supervision. Obviously, the initial
scene-specific detector, initialized by several bounding boxes, exhibits poor
detection performance and is difficult to improve with traditional online
learning algorithm. Consequently, we propose Generative-Discriminative model to
partition detection response space and assign each partition an individual
descriptor that progressively achieves high classification accuracy. A novel
online gradual optimized process is proposed to optimize the
Generative-Discriminative model and focus on the hard samples.Experimental
results on six video datasets show our approach achieves comparable performance
to robust supervised methods, and outperforms the state of the art
self-learning methods under varying imaging conditions.

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The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph