# Multimodal Graph Network Modeling for Human-Object Interaction Detection with PDE Graph Diffusion

arXiv: https://arxiv.org/abs/2509.12554 · HTML: https://mlsanity.com/paper/2509.12554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 40 of its 62 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Detecting and Recognizing Human-Object Interactions](https://mlsanity.com/paper/1704.07333/) (2017)
- [Visual Semantic Role Labeling](https://mlsanity.com/paper/1505.04474/) (2015)
- [Learning Human-Object Interactions by Graph Parsing Neural Networks](https://mlsanity.com/paper/1808.07962/) (2018)
- [DETRs with Hybrid Matching](https://mlsanity.com/paper/2207.13080/) (2022)
- [PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection](https://mlsanity.com/paper/1912.12898/) (2019)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [DRG: Dual Relation Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2008.11714/) (2020)
- [Reformulating HOI Detection as Adaptive Set Prediction](https://mlsanity.com/paper/2103.05983/) (2021)
- [Spatially Conditioned Graphs for Detecting Human-Object Interactions](https://mlsanity.com/paper/2012.06060/) (2020)
- [Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer](https://mlsanity.com/paper/2112.01838/) (2021)
- [HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models](https://mlsanity.com/paper/2303.15786/) (2023)
- [Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection](https://mlsanity.com/paper/2104.05269/) (2021)
- [Exploring Predicate Visual Context in Detecting Human-Object Interactions](https://mlsanity.com/paper/2308.06202/) (2023)
- [RLIPv2: Fast Scaling of Relational Language-Image Pre-training](https://mlsanity.com/paper/2308.09351/) (2023)
- [ViPLO: Vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2304.08114/) (2023)
- [Human-Object Interaction Detection via Disentangled Transformer](https://mlsanity.com/paper/2204.09290/) (2022)
- [Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory](https://mlsanity.com/paper/2309.03696/) (2023)
- [UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection](https://mlsanity.com/paper/2312.12664/) (2023)
- [Interact as You Intend: Intention-Driven Human-Object Interaction Detection](https://mlsanity.com/paper/1808.09796/) (2018)
- [FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection](https://mlsanity.com/paper/2301.04019/) (2023)
- [Open-World Human-Object Interaction Detection via Multi-modal Prompts](https://mlsanity.com/paper/2406.07221/) (2024)
- [EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection](https://mlsanity.com/paper/2410.23904/) (2024)
- [Guiding Human-Object Interactions with Rich Geometry and Relations](https://mlsanity.com/paper/2503.20172/) (2025)

## Abstract (arXiv)

Existing GNN-based Human-Object Interaction (HOI) detection methods rely on simple MLPs to fuse instance features and propagate information. However, this mechanism is largely empirical and lack of targeted information propagation process. To address this problem, we propose Multimodal Graph Network Modeling (MGNM) for HOI detection with Partial Differential Equation (PDE) graph diffusion. Specifically, we first design a multimodal graph network framework that explicitly models the HOI detection task within a four-stage graph structure. Next, we propose a novel PDE diffusion mechanism to facilitate information propagation within this graph. This mechanism leverages multimodal features to propaganda information via a white-box PDE diffusion equation. Furthermore, we design a variational information squeezing (VIS) mechanism to further refine the multimodal features extracted from CLIP, thereby mitigating the impact of noise inherent in pretrained Vision-Language Models. Extensive experiments demonstrate that our MGNM achieves state-of-the-art performance on two widely used benchmarks: HICO-DET and V-COCO. Moreover, when integrated with a more advanced object detector, our method yields significant performance gains while maintaining an effective balance between rare and non-rare categories.