Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Active Learning for Video-based Person Re-identification   It is prohibitively expensive to annotate a large-scale video-based person
re-identification (re-ID) dataset, which makes fully supervised methods
inapplicable to real-world deployment. How to maximally reduce the annotation
cost while retaining the re-ID performance becomes an interesting problem. In
this paper, we address this problem by integrating an active learning scheme
into a deep learning framework. Noticing that the truly matched tracklet-pairs,
also denoted as true positives (TP), are the most informative samples for our
re-ID model, we propose a sampling criterion to choose the most TP-likely
tracklet-pairs for annotation. A view-aware sampling strategy considering
view-specific biases is designed to facilitate candidate selection, followed by
an adaptive resampling step to leave out the selected candidates that are
unnecessary to annotate. Our method learns the re-ID model and updates the
annotation set iteratively. The re-ID model is supervised by the tracklets'
pesudo labels that are initialized by treating each tracklet as a distinct
class. With the gained annotations of the actively selected candidates, the
tracklets' pesudo labels are updated by label merging and further used to
re-train our re-ID model. While being simple, the proposed method demonstrates
its effectiveness on three video-based person re-ID datasets. Experimental
results show that less than 3\% pairwise annotations are needed for our method
to reach comparable performance with the fully-supervised setting.

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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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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-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th