Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Modeling for Task Recognition and Action Segmentation in
  Weakly-Labeled Instructional Videos   This paper focuses on task recognition and action segmentation in
weakly-labeled instructional videos, where only the ordered sequence of
video-level actions is available during training. We propose a two-stream
framework, which exploits semantic and temporal hierarchies to recognize
top-level tasks in instructional videos. Further, we present a novel top-down
weakly-supervised action segmentation approach, where the predicted task is
used to constrain the inference of fine-grained action sequences. Experimental
results on the popular Breakfast and Cooking 2 datasets show that our
two-stream hierarchical task modeling significantly outperforms existing
methods in top-level task recognition for all datasets and metrics.
Additionally, using our task recognition framework in the proposed top-down
action segmentation approach consistently improves the state of the art, while
also reducing segmentation inference time by 80-90 percent.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
19
The categories of this paper are: eess
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math