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.

翻译：统一视觉定位旨在通过更少的任务特定设计，利用多任务数据实现简单通用的技术路线。最先进的方法通常将框和掩码表示为顶点序列，从而将指代检测与分割建模为自回归的序列顶点生成范式。然而，顺序生成高维顶点序列容易出错，因为序列的上游部分保持静态，无法根据下游顶点信息进行修正，即使存在显著的位置偏差。此外，受限于有限顶点数，复杂轮廓目标的拟合效果不佳，进一步限制了性能上限。为解决这一困境，我们提出一种基于扩散模型的并行顶点生成范式，仅需修改噪声维度即可实现高维可扩展性。该范式的直观实现是并行顶点扩散（PVD），直接以顶点坐标为生成目标，利用扩散模型进行训练与推理。我们认为该方案存在两个缺陷：（1）未归一化的坐标导致损失值方差较大；（2）PVD的原始训练目标仅考虑点一致性而忽略几何一致性。针对第一个缺陷，我们设计中心锚点机制（CAM），将坐标转换为归一化偏移值以稳定训练损失值。针对第二个缺陷，我们提出角度求和损失（ASL），约束预测顶点与真实顶点的几何差异，实现几何级一致性。实验结果表明，我们的PVD在指代检测与分割任务上均达到最优性能，且该范式在高维数据场景下比序列顶点生成更具可扩展性与效率。