# Parallel Vertex Diffusion for Unified Visual Grounding

arXiv: https://arxiv.org/abs/2303.07216 · HTML: https://mlsanity.com/paper/2303.07216/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 71 of its 87 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 71)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://mlsanity.com/paper/1801.08186/) (2018)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning](https://mlsanity.com/paper/2208.04202/) (2022)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [CRIS: CLIP-Driven Referring Image Segmentation](https://mlsanity.com/paper/2111.15174/) (2021)
- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [LAVT: Language-Aware Vision Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2112.02244/) (2021)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)

## Built on by (most cited first, 28 of 28)

- [DiffusionRet: Generative Text-Video Retrieval with Diffusion Model](https://mlsanity.com/paper/2303.09867/) (2023)
- [Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs](https://mlsanity.com/paper/2311.01015/) (2023)
- [Mask Grounding for Referring Image Segmentation](https://mlsanity.com/paper/2312.12198/) (2023)
- [Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models](https://mlsanity.com/paper/2308.16777/) (2023)
- [Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment](https://mlsanity.com/paper/2305.12218/) (2023)
- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [An Efficient and Effective Transformer Decoder-Based Framework for Multi-Task Visual Grounding](https://mlsanity.com/paper/2408.01120/) (2024)
- [FreestyleRet: Retrieving Images from Style-Diversified Queries](https://mlsanity.com/paper/2312.02428/) (2023)
- [Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints](https://mlsanity.com/paper/2501.06710/) (2025)
- [DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback Synergy](https://mlsanity.com/paper/2507.01738/) (2025)
- [Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation](https://mlsanity.com/paper/2407.10528/) (2024)
- [PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination](https://mlsanity.com/paper/2509.04833/) (2025)
- [Improving Generalized Visual Grounding with Instance-aware Joint Learning](https://mlsanity.com/paper/2509.13747/) (2025)
- [Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning](https://mlsanity.com/paper/2305.18158/) (2023)
- [Hierarchical Banzhaf Interaction for General Video-Language Representation Learning](https://mlsanity.com/paper/2412.20964/) (2024)
- [Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception](https://mlsanity.com/paper/2504.11457/) (2025)
- [Progressive Language-guided Visual Learning for Multi-Task Visual Grounding](https://mlsanity.com/paper/2504.16145/) (2025)
- [DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2308.15109/) (2023)
- [Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation](https://mlsanity.com/paper/2404.08281/) (2024)
- [Referencing Where to Focus: Improving VisualGrounding with Referential Query](https://mlsanity.com/paper/2412.19155/) (2024)
- [SSP-SAM: SAM with Semantic-Spatial Prompt for Referring Expression Segmentation](https://mlsanity.com/paper/2603.18086/) (2026)
- [EAVL: Explicitly Align Vision and Language for Referring Image Segmentation](https://mlsanity.com/paper/2308.09779/) (2023)
- [Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation](https://mlsanity.com/paper/2405.11205/) (2024)
- [GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions](https://mlsanity.com/paper/2508.21102/) (2025)
- [A Simple and Better Baseline for Visual Grounding](https://mlsanity.com/paper/2510.10587/) (2025)
- [Semantic-Spatial Discriminability Enhancement for Generalized Visual Grounding](https://mlsanity.com/paper/2608.30233/) (2026)
- [DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding](https://mlsanity.com/paper/2609.24409/) (2026)
- [Groundbench: Multi-Resolution Polygon Grounding Exposes the Geometry Gap in Vision-Language Models](https://mlsanity.com/paper/2609.27821/) (2026)

## Abstract (arXiv)

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.