Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attentive Contexts for Object Detection   Modern deep neural network based object detection methods typically classify
candidate proposals using their interior features. However, global and local
surrounding contexts that are believed to be valuable for object detection are
not fully exploited by existing methods yet. In this work, we take a step
towards understanding what is a robust practice to extract and utilize
contextual information to facilitate object detection in practice.
Specifically, we consider the following two questions: "how to identify useful
global contextual information for detecting a certain object?" and "how to
exploit local context surrounding a proposal for better inferring its
contents?". We provide preliminary answers to these questions through
developing a novel Attention to Context Convolution Neural Network (AC-CNN)
based object detection model. AC-CNN effectively incorporates global and local
contextual information into the region-based CNN (e.g. Fast RCNN) detection
model and provides better object detection performance. It consists of one
attention-based global contextualized (AGC) sub-network and one multi-scale
local contextualized (MLC) sub-network. To capture global context, the AGC
sub-network recurrently generates an attention map for an input image to
highlight useful global contextual locations, through multiple stacked Long
Short-Term Memory (LSTM) layers. For capturing surrounding local context, the
MLC sub-network exploits both the inside and outside contextual information of
each specific proposal at multiple scales. The global and local context are
then fused together for making the final decision for detection. Extensive
experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of
the proposed AC-CNN over well-established baselines. In particular, AC-CNN
outperforms the popular Fast-RCNN by 2.0% and 2.2% on VOC 2007 and VOC 2012 in
terms of mAP, respectively.

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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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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: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc