Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Segregated Temporal Assembly Recurrent Networks for Weakly Supervised
  Multiple Action Detection   This paper proposes a segregated temporal assembly recurrent (STAR) network
for weakly-supervised multiple action detection. The model learns from
untrimmed videos with only supervision of video-level labels and makes
prediction of intervals of multiple actions. Specifically, we first assemble
video clips according to class labels by an attention mechanism that learns
class-variable attention weights and thus helps the noise relieving from
background or other actions. Secondly, we build temporal relationship between
actions by feeding the assembled features into an enhanced recurrent neural
network. Finally, we transform the output of recurrent neural network into the
corresponding action distribution. In order to generate more precise temporal
proposals, we design a score term called segregated temporal gradient-weighted
class activation mapping (ST-GradCAM) fused with attention weights. Experiments
on THUMOS'14 and ActivityNet1.3 datasets show that our approach outperforms the
state-of-the-art weakly-supervised method, and performs at par with the
fully-supervised counterparts.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg