Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Learning On The Hierarchy Representation for Fine-Grained Human
  Action Recognition   Fine-grained human action recognition is a core research topic in computer
vision. Inspired by the recently proposed hierarchy representation of
fine-grained actions in FineGym and SlowFast network for action recognition, we
propose a novel multi-task network which exploits the FineGym hierarchy
representation to achieve effective joint learning and prediction for
fine-grained human action recognition. The multi-task network consists of three
pathways of SlowOnly networks with gradually increased frame rates for events,
sets and elements of fine-grained actions, followed by our proposed integration
layers for joint learning and prediction. It is a two-stage approach, where it
first learns deep feature representation at each hierarchical level, and is
followed by feature encoding and fusion for multi-task learning. Our empirical
results on the FineGym dataset achieve a new state-of-the-art performance, with
91.80% Top-1 accuracy and 88.46% mean accuracy for element actions, which are
3.40% and 7.26% higher than the previous best results.

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex