Video Action Recognition Via Neural Architecture Searching
By Wei Peng, Xiaopeng Hong, Guoying Zhao. 2019
39 citations recorded by the source corpus.
Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to evaluate each architecture candidate is still heavy. To alleviate this issue, we, for the video input, introduce a temporal segment approach to reduce the computational cost without losing global video information. For the architecture, we explore in an efficient search space by introducing pseudo 3D operators. Experiments show that, our architecture outperforms popular neural architectures, under the training from scratch protocol, on the challenging UCF101 dataset, surprisingly, with only around one percentage of parameters of its manual-design counterparts.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML
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Citations in the corpus
- Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, methodology): “For the perspective of computer vision applications, NAS has been developed for face recognition [67], action recognition [46], person ReID [50], object detection [21] and segmentation [65] tasks.”