  Action detection aims to localize the starting and ending points of action
instances in untrimmed videos, and predict the classes of those instances. In
this paper, we make the observation that the outputs of the action detection
task can be formulated as images. Thus, from a novel perspective, we tackle
action detection via a three-image generation process to generate starting
point, ending point and action-class predictions as images via our proposed
Action Detection Image Diffusion (ADI-Diff) framework. Furthermore, since our
images differ from natural images and exhibit special properties, we further
explore a Discrete Action-Detection Diffusion Process and a Row-Column
Transformer design to better handle their processing. Our ADI-Diff framework
achieves state-of-the-art results on two widely-used datasets.
