Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diagnosing Error in Temporal Action Detectors   Despite the recent progress in video understanding and the continuous rate of
improvement in temporal action localization throughout the years, it is still
unclear how far (or close?) we are to solving the problem. To this end, we
introduce a new diagnostic tool to analyze the performance of temporal action
detectors in videos and compare different methods beyond a single scalar
metric. We exemplify the use of our tool by analyzing the performance of the
top rewarded entries in the latest ActivityNet action localization challenge.
Our analysis shows that the most impactful areas to work on are: strategies to
better handle temporal context around the instances, improving the robustness
w.r.t. the instance absolute and relative size, and strategies to reduce the
localization errors. Moreover, our experimental analysis finds the lack of
agreement among annotator is not a major roadblock to attain progress in the
field. Our diagnostic tool is publicly available to keep fueling the minds of
other researchers with additional insights about their algorithms.

26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga