  In the field of complex action recognition in videos, the quality of the
designed model plays a crucial role in the final performance. However,
artificially designed network structures often rely heavily on the researchers'
knowledge and experience. Accordingly, because of the automated design of its
network structure, Neural architecture search (NAS) has achieved great success
in the image processing field and attracted substantial research attention in
recent years. Although some NAS methods have reduced the number of GPU search
days required to single digits in the image field, directly using 3D
convolution to extend NAS to the video field is still likely to produce a surge
in computing volume. To address this challenge, we propose a new processing
framework called Neural Architecture Search- Temporal Convolutional (NAS-TC).
Our proposed framework is divided into two phases. In the first phase, the
classical CNN network is used as the backbone network to complete the
computationally intensive feature extraction task. In the second stage, a
simple stitching search to the cell is used to complete the relatively
lightweight long-range temporal-dependent information extraction. This ensures
our method will have more reasonable parameter assignments and can handle
minute-level videos. Finally, we conduct sufficient experiments on multiple
benchmark datasets and obtain competitive recognition accuracy.
