Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Representation Learning by Sorting Sequences   We present an unsupervised representation learning approach using videos
without semantic labels. We leverage the temporal coherence as a supervisory
signal by formulating representation learning as a sequence sorting task. We
take temporally shuffled frames (i.e., in non-chronological order) as inputs
and train a convolutional neural network to sort the shuffled sequences.
Similar to comparison-based sorting algorithms, we propose to extract features
from all frame pairs and aggregate them to predict the correct order. As
sorting shuffled image sequence requires an understanding of the statistical
temporal structure of images, training with such a proxy task allows us to
learn rich and generalizable visual representation. We validate the
effectiveness of the learned representation using our method as pre-training on
high-level recognition problems. The experimental results show that our method
compares favorably against state-of-the-art methods on action recognition,
image classification and object detection tasks.

14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph