  Unified visual grounding pursues a simple and generic technical route to
leverage multi-task data with less task-specific design. The most advanced
methods typically present boxes and masks as vertex sequences to model
referring detection and segmentation as an autoregressive sequential vertex
generation paradigm. However, generating high-dimensional vertex sequences
sequentially is error-prone because the upstream of the sequence remains static
and cannot be refined based on downstream vertex information, even if there is
a significant location gap. Besides, with limited vertexes, the inferior
fitting of objects with complex contours restricts the performance upper bound.
To deal with this dilemma, we propose a parallel vertex generation paradigm for
superior high-dimension scalability with a diffusion model by simply modifying
the noise dimension. An intuitive materialization of our paradigm is Parallel
Vertex Diffusion (PVD) to directly set vertex coordinates as the generation
target and use a diffusion model to train and infer. We claim that it has two
flaws: (1) unnormalized coordinate caused a high variance of loss value; (2)
the original training objective of PVD only considers point consistency but
ignores geometry consistency. To solve the first flaw, Center Anchor Mechanism
(CAM) is designed to convert coordinates as normalized offset values to
stabilize the training loss value. For the second flaw, Angle summation loss
(ASL) is designed to constrain the geometry difference of prediction and ground
truth vertexes for geometry-level consistency. Empirical results show that our
PVD achieves state-of-the-art in both referring detection and segmentation, and
our paradigm is more scalable and efficient than sequential vertex generation
with high-dimension data.
