Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action
  Classifier for Anomaly Detection   Video anomaly detection under weak labels is formulated as a typical
multiple-instance learning problem in previous works. In this paper, we provide
a new perspective, i.e., a supervised learning task under noisy labels. In such
a viewpoint, as long as cleaning away label noise, we can directly apply fully
supervised action classifiers to weakly supervised anomaly detection, and take
maximum advantage of these well-developed classifiers. For this purpose, we
devise a graph convolutional network to correct noisy labels. Based upon
feature similarity and temporal consistency, our network propagates supervisory
signals from high-confidence snippets to low-confidence ones. In this manner,
the network is capable of providing cleaned supervision for action classifiers.
During the test phase, we only need to obtain snippet-wise predictions from the
action classifier without any extra post-processing. Extensive experiments on 3
datasets at different scales with 2 types of action classifiers demonstrate the
efficacy of our method. Remarkably, we obtain the frame-level AUC score of
82.12% on UCF-Crime.

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The categories of this paper are: q-alg
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ