Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Looking Twice for Partial Clues: Weakly-supervised Part-Mentored
  Attention Network for Vehicle Re-Identification   Vehicle re-identification (Re-ID) is to retrieve images of the same vehicle
across different cameras. Two key challenges lie in the subtle inter-instance
discrepancy caused by near-duplicate identities and the large intra-instance
variance caused by different views. Since the holistic appearance suffers from
viewpoint variation and distortion, part-level feature learning has been
introduced to enhance vehicle description. However, existing approaches to
localize and amplify significant parts often fail to handle spatial
misalignment as well as occlusion and require expensive annotations. In this
paper, we propose a weakly supervised Part-Mentored Attention Network (PMANet)
composed of a Part Attention Network (PANet) for vehicle part localization with
self-attention and a Part-Mentored Network (PMNet) for mentoring the global and
local feature aggregation. Firstly, PANet is introduced to predict a foreground
mask and pinpoint $K$ prominent vehicle parts only with weak identity
supervision. Secondly, we propose a PMNet to learn global and part-level
features with multi-scale attention and aggregate them in $K$ main-partial
tasks via part transfer. Like humans who first differentiate objects with
general information and then observe salient parts for more detailed clues,
PANet and PMNet construct a two-stage attention structure to perform a
coarse-to-fine search among identities. Finally, we address this Re-ID issue as
a multi-task problem, including global feature learning, identity
classification, and part transfer. We adopt Homoscedastic Uncertainty to learn
the optimal weighing of different losses. Comprehensive experiments are
conducted on two benchmark datasets. Our approach outperforms recent
state-of-the-art methods by averagely 2.63% in CMC@1 on VehicleID and 2.2% in
mAP on VeRi776. Results on occluded test sets also demonstrate the
generalization ability of PMANet.

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The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin