# Spatially Conditioned Graphs for Detecting Human-Object Interactions

arXiv: https://arxiv.org/abs/2012.06060 · HTML: https://mlsanity.com/paper/2012.06060/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 30 of 93)

- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [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)
- [Exploring Predicate Visual Context in Detecting Human-Object Interactions](https://mlsanity.com/paper/2308.06202/) (2023)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection](https://mlsanity.com/paper/2206.06291/) (2022)
- [ViPLO: Vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2304.08114/) (2023)
- [Interactiveness Field in Human-Object Interactions](https://mlsanity.com/paper/2204.07718/) (2022)
- [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)
- [Knowledge Composition using Task Vectors with Learned Anisotropic Scaling](https://mlsanity.com/paper/2407.02880/) (2024)
- [Relational Context Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2304.04997/) (2023)
- [What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions](https://mlsanity.com/paper/2204.00746/) (2022)
- [Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models](https://mlsanity.com/paper/2311.03799/) (2023)
- [FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection](https://mlsanity.com/paper/2301.04019/) (2023)
- [QAHOI: Query-Based Anchors for Human-Object Interaction Detection](https://mlsanity.com/paper/2112.08647/) (2021)
- [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)
- [Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detection](https://mlsanity.com/paper/2408.02484/) (2024)
- [Neural-Logic Human-Object Interaction Detection](https://mlsanity.com/paper/2311.09817/) (2023)
- [Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection](https://mlsanity.com/paper/2404.06194/) (2024)
- [Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2305.12252/) (2023)
- [Category Query Learning for Human-Object Interaction Classification](https://mlsanity.com/paper/2303.14005/) (2023)
- [Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions](https://mlsanity.com/paper/2311.17048/) (2023)
- [Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows](https://mlsanity.com/paper/2203.10537/) (2022)
- [Collaborative Transformers for Grounded Situation Recognition](https://mlsanity.com/paper/2203.16518/) (2022)
- [Learning from Observer Gaze:Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognition](https://mlsanity.com/paper/2405.09931/) (2024)
- [Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection](https://mlsanity.com/paper/2311.01755/) (2023)
- [Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models](https://mlsanity.com/paper/2410.20155/) (2024)
- [Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection](https://mlsanity.com/paper/2307.13529/) (2023)

## Abstract (arXiv)

We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in different messages going to neighbours of the same node. To this end, we explore various ways of applying spatial conditioning under a multi-branch structure. Through extensive experimentation we demonstrate the advantages of spatial conditioning for the computation of the adjacency structure, messages and the refined graph features. In particular, we empirically show that as the quality of the bounding boxes increases, their coarse appearance features contribute relatively less to the disambiguation of interactions compared to the spatial information. Our method achieves an mAP of 31.33% on HICO-DET and 54.2% on V-COCO, significantly outperforming state-of-the-art on fine-tuned detections.