  In this paper, we propose a new framework called YOWOv3, which is an improved
version of YOWOv2, designed specifically for the task of Human Action Detection
and Recognition. This framework is designed to facilitate extensive
experimentation with different configurations and supports easy customization
of various components within the model, reducing efforts required for
understanding and modifying the code. YOWOv3 demonstrates its superior
performance compared to YOWOv2 on two widely used datasets for Human Action
Detection and Recognition: UCF101-24 and AVAv2.2. Specifically, the predecessor
model YOWOv2 achieves an mAP of 85.2% and 20.3% on UCF101-24 and AVAv2.2,
respectively, with 109.7M parameters and 53.6 GFLOPs. In contrast, our model -
YOWOv3, with only 59.8M parameters and 39.8 GFLOPs, achieves an mAP of 88.33%
and 20.31% on UCF101-24 and AVAv2.2, respectively. The results demonstrate that
YOWOv3 significantly reduces the number of parameters and GFLOPs while still
achieving comparable performance.
