Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Capturing Temporal Information in a Single Frame: Channel Sampling
  Strategies for Action Recognition   We address the problem of capturing temporal information for video
classification in 2D networks, without increasing computational cost. Existing
approaches focus on modifying the architecture of 2D networks (e.g. by
including filters in the temporal dimension to turn them into 3D networks, or
using optical flow, etc.), which increases computation cost. Instead, we
propose a novel sampling strategy, where we re-order the channels of the input
video, to capture short-term frame-to-frame changes. We observe that without
bells and whistles, the proposed sampling strategy improves performance on
multiple architectures (e.g. TSN, TRN, and TSM) and datasets (CATER,
Something-Something-V1 and V2), up to 24% over the baseline of using the
standard video input. In addition, our sampling strategies do not require
training from scratch and do not increase the computational cost of training
and testing. Given the generality of the results and the flexibility of the
approach, we hope this can be widely useful to the video understanding
community. Code is available at https://github.com/kiyoon/PyVideoAI.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat