# Mining Cross-Person Cues for Body-Part Interactiveness Learning in HOI Detection

arXiv: https://arxiv.org/abs/2207.14192 · HTML: https://mlsanity.com/paper/2207.14192/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 39 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 30 of 35)

- [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)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Detecting and Recognizing Human-Object Interactions](https://mlsanity.com/paper/1704.07333/) (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)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (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)
- [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)
- [Detailed 2D-3D Joint Representation for Human-Object Interaction](https://mlsanity.com/paper/2004.08154/) (2020)
- [Pose-aware Multi-level Feature Network for Human Object Interaction Detection](https://mlsanity.com/paper/1909.08453/) (2019)
- [HOI Analysis: Integrating and Decomposing Human-Object Interaction](https://mlsanity.com/paper/2010.16219/) (2020)
- [PaStaNet: Toward Human Activity Knowledge Engine](https://mlsanity.com/paper/2004.00945/) (2020)
- [Detecting Human-Object Interaction via Fabricated Compositional Learning](https://mlsanity.com/paper/2103.08214/) (2021)
- [Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection](https://mlsanity.com/paper/2104.05269/) (2021)
- [Interactiveness Field in Human-Object Interactions](https://mlsanity.com/paper/2204.07718/) (2022)
- [Pairwise Body-Part Attention for Recognizing Human-Object Interactions](https://mlsanity.com/paper/1807.10889/) (2018)

## Built on by (most cited first, 30 of 34)

- [InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion](https://mlsanity.com/paper/2308.16905/) (2023)
- [PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction](https://mlsanity.com/paper/2312.04393/) (2023)
- [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)
- [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)
- [Open-World Human-Object Interaction Detection via Multi-modal Prompts](https://mlsanity.com/paper/2406.07221/) (2024)
- [Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detection](https://mlsanity.com/paper/2408.02484/) (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)
- [From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding](https://mlsanity.com/paper/2304.00553/) (2023)
- [Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models](https://mlsanity.com/paper/2410.20155/) (2024)
- [Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario](https://mlsanity.com/paper/2306.12152/) (2023)
- [Unified Visual Relationship Detection with Vision and Language Models](https://mlsanity.com/paper/2303.08998/) (2023)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [Beyond Object Recognition: A New Benchmark towards Object Concept Learning](https://mlsanity.com/paper/2212.02710/) (2022)
- [Diagnosing Human-object Interaction Detectors](https://mlsanity.com/paper/2308.08529/) (2023)
- [Primitive-based 3D Human-Object Interaction Modelling and Programming](https://mlsanity.com/paper/2312.10714/) (2023)
- [Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection](https://mlsanity.com/paper/2401.05676/) (2024)
- [Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions](https://mlsanity.com/paper/2211.07501/) (2022)
- [Interacted Object Grounding in Spatio-Temporal Human-Object Interactions](https://mlsanity.com/paper/2412.19542/) (2024)
- [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)
- [BPJDet: Extended Object Representation for Generic Body-Part Joint Detection](https://mlsanity.com/paper/2304.10765/) (2023)
- [Geometric Features Enhanced Human-Object Interaction Detection](https://mlsanity.com/paper/2406.18691/) (2024)
- [ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios](https://mlsanity.com/paper/2603.09741/) (2026)
- [TMHOI: Translational Model for Human-Object Interaction Detection](https://mlsanity.com/paper/2303.04253/) (2023)
- [An analysis of HOI: using a training-free method with multimodal visual foundation models when only the test set is available, without the training set](https://mlsanity.com/paper/2408.05772/) (2024)
- [DQEN: Dual Query Enhancement Network for DETR-based HOI Detection](https://mlsanity.com/paper/2508.18896/) (2025)
- [UV R-CNN: Stable and Efficient Dense Human Pose Estimation](https://mlsanity.com/paper/2211.02337/) (2022)

## Abstract (arXiv)

Human-Object Interaction (HOI) detection plays a crucial role in activity understanding. Though significant progress has been made, interactiveness learning remains a challenging problem in HOI detection: existing methods usually generate redundant negative H-O pair proposals and fail to effectively extract interactive pairs. Though interactiveness has been studied in both whole body- and part- level and facilitates the H-O pairing, previous works only focus on the target person once (i.e., in a local perspective) and overlook the information of the other persons. In this paper, we argue that comparing body-parts of multi-person simultaneously can afford us more useful and supplementary interactiveness cues. That said, to learn body-part interactiveness from a global perspective: when classifying a target person's body-part interactiveness, visual cues are explored not only from herself/himself but also from other persons in the image. We construct body-part saliency maps based on self-attention to mine cross-person informative cues and learn the holistic relationships between all the body-parts. We evaluate the proposed method on widely-used benchmarks HICO-DET and V-COCO. With our new perspective, the holistic global-local body-part interactiveness learning achieves significant improvements over state-of-the-art. Our code is available at https://github.com/enlighten0707/Body-Part-Map-for-Interactiveness.