# End-to-End Human Object Interaction Detection with HOI Transformer

arXiv: https://arxiv.org/abs/2103.04503 · HTML: https://mlsanity.com/paper/2103.04503/

## Key facts

- Cited by 135 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Parallel WaveNet: Fast High-Fidelity Speech Synthesis](https://mlsanity.com/paper/1711.10433/) (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)
- [PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection](https://mlsanity.com/paper/1912.12898/) (2019)
- [End-to-end people detection in crowded scenes](https://mlsanity.com/paper/1506.04878/) (2015)
- [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)
- [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)
- [Pose-aware Multi-level Feature Network for Human Object Interaction Detection](https://mlsanity.com/paper/1909.08453/) (2019)
- [PaStaNet: Toward Human Activity Knowledge Engine](https://mlsanity.com/paper/2004.00945/) (2020)
- [Detecting Human-Object Interactions with Action Co-occurrence Priors](https://mlsanity.com/paper/2007.08728/) (2020)
- [Detecting Human-Object Interactions via Functional Generalization](https://mlsanity.com/paper/1904.03181/) (2019)
- [Contextual Heterogeneous Graph Network for Human-Object Interaction Detection](https://mlsanity.com/paper/2010.10001/) (2020)
- [Cascaded Human-Object Interaction Recognition](https://mlsanity.com/paper/2003.04262/) (2020)

## Built on by (most cited first, 30 of 135)

- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [Panoptic Scene Graph Generation](https://mlsanity.com/paper/2207.11247/) (2022)
- [MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer](https://mlsanity.com/paper/2203.10981/) (2022)
- [Mining the Benefits of Two-stage and One-stage HOI Detection](https://mlsanity.com/paper/2108.05077/) (2021)
- [SGTR: End-to-end Scene Graph Generation with Transformer](https://mlsanity.com/paper/2112.12970/) (2021)
- [RelTR: Relation Transformer for Scene Graph Generation](https://mlsanity.com/paper/2201.11460/) (2022)
- [Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer](https://mlsanity.com/paper/2112.01838/) (2021)
- [KVT: k-NN Attention for Boosting Vision Transformers](https://mlsanity.com/paper/2106.00515/) (2021)
- [HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models](https://mlsanity.com/paper/2303.15786/) (2023)
- [Human-Aware Object Placement for Visual Environment Reconstruction](https://mlsanity.com/paper/2203.03609/) (2022)
- [Exploring Predicate Visual Context in Detecting Human-Object Interactions](https://mlsanity.com/paper/2308.06202/) (2023)
- [RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection](https://mlsanity.com/paper/2209.01814/) (2022)
- [DECO: Dense Estimation of 3D Human-Scene Contact In The Wild](https://mlsanity.com/paper/2309.15273/) (2023)
- [LEMON: Learning 3D Human-Object Interaction Relation from 2D Images](https://mlsanity.com/paper/2312.08963/) (2023)
- [Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection](https://mlsanity.com/paper/2206.06291/) (2022)
- [Webly Supervised Concept Expansion for General Purpose Vision Models](https://mlsanity.com/paper/2202.02317/) (2022)
- [RLIPv2: Fast Scaling of Relational Language-Image Pre-training](https://mlsanity.com/paper/2308.09351/) (2023)
- [Structured Sparse R-CNN for Direct Scene Graph Generation](https://mlsanity.com/paper/2106.10815/) (2021)
- [AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2403.17934/) (2024)
- [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)
- [EDTER: Edge Detection with Transformer](https://mlsanity.com/paper/2203.08566/) (2022)
- [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)
- [MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection](https://mlsanity.com/paper/2203.14709/) (2022)

## Abstract (arXiv)

We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamlines the HOI pipeline by eliminating the need for many hand-designed components. HOI Transformer reasons about the relations of objects and humans from global image context and directly predicts HOI instances in parallel. A quintuple matching loss is introduced to force HOI predictions in a unified way. Our method is conceptually much simpler and demonstrates improved accuracy. Without bells and whistles, HOI Transformer achieves 26.61%   AP  on HICO-DET and 52.9% AProle on V-COCO, surpassing previous methods with the advantage of being much simpler. We hope our approach will serve as a simple and effective alternative for HOI tasks. Code is available at https://github.com/bbepoch/HoiTransformer .