Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Progression Modelling for Online and Early Gesture Detection   Online and Early detection of gestures is crucial for building touchless
gesture based interfaces. These interfaces should operate on a stream of video
frames instead of the complete video and detect the presence of gestures at an
earlier stage than post-completion for providing real time user experience. To
achieve this, it is important to recognize the progression of the gesture
across different stages so that appropriate responses can be triggered on
reaching the desired execution stage. To address this, we propose a simple yet
effective multi-task learning framework which models the progression of the
gesture along with frame level recognition. The proposed framework recognizes
the gestures at an early stage with high precision and also achieves
state-of-the-art recognition accuracy of 87.8% which is closer to human
accuracy of 88.4% on the NVIDIA gesture dataset in the offline configuration
and advances the state-of-the-art by more than 4%. We also introduce tightly
segmented annotations for the NVIDIA gesture dataset and setup a strong
baseline for gesture localization for this dataset. We also evaluate our
framework on the Montalbano dataset and report competitive results.

15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat