Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Future Frame Prediction Using Convolutional VRNN for Anomaly Detection   Anomaly detection in videos aims at reporting anything that does not conform
the normal behaviour or distribution. However, due to the sparsity of abnormal
video clips in real life, collecting annotated data for supervised learning is
exceptionally cumbersome. Inspired by the practicability of generative models
for semi-supervised learning, we propose a novel sequential generative model
based on variational autoencoder (VAE) for future frame prediction with
convolutional LSTM (ConvLSTM). To the best of our knowledge, this is the first
work that considers temporal information in future frame prediction based
anomaly detection framework from the model perspective. Our experiments
demonstrate that our approach is superior to the state-of-the-art methods on
three benchmark datasets.

20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
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The categories of this paper are: eess
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph