  In this technical report, we would like to introduce our updates to YOWO, a
real-time method for spatio-temporal action detection. We make a bunch of
little design changes to make it better. For network structure, we use the same
ones of official implemented YOWO, including 3D-ResNext-101 and YOLOv2, but we
use a better pretrained weight of our reimplemented YOLOv2, which is better
than the official YOLOv2. We also optimize the label assignment used in YOWO.
To accurately detection action instances, we deploy GIoU loss for box
regression. After our incremental improvement, YOWO achieves 84.9\% frame mAP
and 50.5\% video mAP on the UCF101-24, significantly higher than the official
YOWO. On the AVA, our optimized YOWO achieves 20.6\% frame mAP with 16 frames,
also exceeding the official YOWO. With 32 frames, our YOWO achieves 21.6 frame
mAP with 25 FPS on an RTX 3090 GPU. We name the optimized YOWO as YOWO-Plus.
Moreover, we replace the 3D-ResNext-101 with the efficient 3D-ShuffleNet-v2 to
design a lightweight action detector, YOWO-Nano. YOWO-Nano achieves 81.0 \%
frame mAP and 49.7\% video frame mAP with over 90 FPS on the UCF101-24. It also
achieves 18.4 \% frame mAP with about 90 FPS on the AVA. As far as we know,
YOWO-Nano is the fastest state-of-the-art action detector. Our code is
available on https://github.com/yjh0410/PyTorch_YOWO.
