# ViPLO: Vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/2304.08114 · HTML: https://mlsanity.com/paper/2304.08114/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 58 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 30 of 45)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Visual Semantic Role Labeling](https://mlsanity.com/paper/1505.04474/) (2015)
- [HOTR: End-to-End Human-Object Interaction Detection with Transformers](https://mlsanity.com/paper/2104.13682/) (2021)
- [Learning Human-Object Interactions by Graph Parsing Neural Networks](https://mlsanity.com/paper/1808.07962/) (2018)
- [QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information](https://mlsanity.com/paper/2103.05399/) (2021)
- [PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection](https://mlsanity.com/paper/1912.12898/) (2019)
- [End-to-End Human Object Interaction Detection with HOI Transformer](https://mlsanity.com/paper/2103.04503/) (2021)
- [Visual Compositional Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2007.12407/) (2020)
- [iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection](https://mlsanity.com/paper/1808.10437/) (2018)
- [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)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [Learning Human-Object Interaction Detection using Interaction Points](https://mlsanity.com/paper/2003.14023/) (2020)
- [Mining the Benefits of Two-stage and One-stage HOI Detection](https://mlsanity.com/paper/2108.05077/) (2021)
- [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)

## Built on by (most cited first, 30 of 41)

- [Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models](https://mlsanity.com/paper/2311.03799/) (2023)
- [HUMOTO: A 4D Dataset of Mocap Human Object Interactions](https://mlsanity.com/paper/2504.10414/) (2025)
- [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)
- [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)
- [Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models](https://mlsanity.com/paper/2410.20155/) (2024)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [HOLa: Zero-Shot HOI Detection with Low-Rank Decomposed VLM Feature Adaptation](https://mlsanity.com/paper/2507.15542/) (2025)
- [CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos](https://mlsanity.com/paper/2406.01029/) (2024)
- [Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration](https://mlsanity.com/paper/2508.03207/) (2025)
- [CycleHOI: Improving Human-Object Interaction Detection with Cycle Consistency of Detection and Generation](https://mlsanity.com/paper/2407.11433/) (2024)
- [An Image-like Diffusion Method for Human-Object Interaction Detection](https://mlsanity.com/paper/2503.18134/) (2025)
- [CrossHOI-Bench: A Unified Benchmark for HOI Evaluation across Vision-Language Models and HOI-Specific Methods](https://mlsanity.com/paper/2508.18753/) (2025)
- [Learning Human-Object Interaction as Groups](https://mlsanity.com/paper/2510.18357/) (2025)
- [Focusing on what to decode and what to train: SOV Decoding with Specific Target Guided DeNoising and Vision Language Advisor](https://mlsanity.com/paper/2307.02291/) (2023)
- [From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2407.00917/) (2024)
- [Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models](https://mlsanity.com/paper/2311.16475/) (2023)
- [Occluded Human Pose Estimation based on Limb Joint Augmentation](https://mlsanity.com/paper/2410.09885/) (2024)
- [SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection](https://mlsanity.com/paper/2503.00414/) (2025)
- [Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2506.03440/) (2025)
- [Egocentric Human-Object Interaction Detection: A New Benchmark and Method](https://mlsanity.com/paper/2506.14189/) (2025)
- [No More Sibling Rivalry: Debiasing Human-Object Interaction Detection](https://mlsanity.com/paper/2509.00760/) (2025)
- [Geometric Features Enhanced Human-Object Interaction Detection](https://mlsanity.com/paper/2406.18691/) (2024)
- [Towards Flexible Visual Relationship Segmentation](https://mlsanity.com/paper/2408.08305/) (2024)
- [3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications](https://mlsanity.com/paper/2410.10782/) (2024)
- [VLM-HOI: Vision Language Models for Interpretable Human-Object Interaction Analysis](https://mlsanity.com/paper/2411.18038/) (2024)
- [How do Foundation Models Compare to Skeleton-Based Approaches for Gesture Recognition in Human-Robot Interaction?](https://mlsanity.com/paper/2506.20795/) (2025)
- [Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache](https://mlsanity.com/paper/2511.18811/) (2025)

## Abstract (arXiv)

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose Vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the Vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the Vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset. The source codes are available at https://github.com/Jeeseung-Park/ViPLO.