Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Counting Out Time: Class Agnostic Video Repetition Counting in the Wild   We present an approach for estimating the period with which an action is
repeated in a video. The crux of the approach lies in constraining the period
prediction module to use temporal self-similarity as an intermediate
representation bottleneck that allows generalization to unseen repetitions in
videos in the wild. We train this model, called Repnet, with a synthetic
dataset that is generated from a large unlabeled video collection by sampling
short clips of varying lengths and repeating them with different periods and
counts. This combination of synthetic data and a powerful yet constrained
model, allows us to predict periods in a class-agnostic fashion. Our model
substantially exceeds the state of the art performance on existing periodicity
(PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new
challenging dataset called Countix (~90 times larger than existing datasets)
which captures the challenges of repetition counting in real-world videos.
Project webpage: https://sites.google.com/view/repnet .

13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys