  We present a conceptually simple, flexible, and universal visual perception
head for variant visual tasks, e.g., classification, object detection, instance
segmentation and pose estimation, and different frameworks, such as one-stage
or two-stage pipelines. Our approach effectively identifies an object in an
image while simultaneously generating a high-quality bounding box or
contour-based segmentation mask or set of keypoints. The method, called
UniHead, views different visual perception tasks as the dispersible points
learning via the transformer encoder architecture. Given a fixed spatial
coordinate, UniHead adaptively scatters it to different spatial points and
reasons about their relations by transformer encoder. It directly outputs the
final set of predictions in the form of multiple points, allowing us to perform
different visual tasks in different frameworks with the same head design. We
show extensive evaluations on ImageNet classification and all three tracks of
the COCO suite of challenges, including object detection, instance segmentation
and pose estimation. Without bells and whistles, UniHead can unify these visual
tasks via a single visual head design and achieve comparable performance
compared to expert models developed for each task.We hope our simple and
universal UniHead will serve as a solid baseline and help promote universal
visual perception research. Code and models are available at
https://github.com/Sense-X/UniHead.
