  The task of spatiotemporal action localization in chaotic scenes is a
challenging task toward advanced video understanding. Paving the way with
high-quality video feature extraction and enhancing the precision of
detector-predicted anchors can effectively improve model performance. To this
end, we propose a high-performance dual-stream spatiotemporal feature
extraction network SFMViT with an anchor pruning strategy. The backbone of our
SFMViT is composed of ViT and SlowFast with prior knowledge of spatiotemporal
action localization, which fully utilizes ViT's excellent global feature
extraction capabilities and SlowFast's spatiotemporal sequence modeling
capabilities. Secondly, we introduce the confidence maximum heap to prune the
anchors detected in each frame of the picture to filter out the effective
anchors. These designs enable our SFMViT to achieve a mAP of 26.62% in the
Chaotic World dataset, far exceeding existing models. Code is available at
https://github.com/jfightyr/SlowFast-Meet-ViT.
