This paper studies the computational offloading of video action recognition
in edge computing. To achieve effective semantic information extraction and
compression, following semantic communication we propose a novel spatiotemporal
attention-based autoencoder (STAE) architecture, including a frame attention
module and a spatial attention module, to evaluate the importance of frames and
pixels in each frame. Additionally, we use entropy encoding to remove
statistical redundancy in the compressed data to further reduce communication
overhead. At the receiver, we develop a lightweight decoder that leverages a
3D-2D CNN combined architecture to reconstruct missing information by
simultaneously learning temporal and spatial information from the received data
to improve accuracy. To fasten convergence, we use a step-by-step approach to
train the resulting STAE-based vision transformer (ViT_STAE) models.
Experimental results show that ViT_STAE can compress the video dataset HMDB51
by 104x with only 5% accuracy loss, outperforming the state-of-the-art baseline
DeepISC. The proposed ViT_STAE achieves faster inference and higher accuracy
than the DeepISC-based ViT model under time-varying wireless channel, which
highlights the effectiveness of STAE in guaranteeing higher accuracy under time
constraints.

本文研究边缘计算中视频动作识别的计算卸载。为了实现有效的语义信息提取，本文提出了一种新的空时注意力自编码器（STAE）结构，包括帧注意力模块和空间注意力模块。实验证明，在时间约束下，与 DeepISC 的基准模型相比，基于 STAE 的 ViT 模型具有更快的推理速度和更高的准确性。