# DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/2010.01005 · HTML: https://mlsanity.com/paper/2010.01005/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [Mining the Benefits of Two-stage and One-stage HOI Detection](https://mlsanity.com/paper/2108.05077/) (2021)
- [Webly Supervised Concept Expansion for General Purpose Vision Models](https://mlsanity.com/paper/2202.02317/) (2022)
- [Human-Object Interaction Detection via Disentangled Transformer](https://mlsanity.com/paper/2204.09290/) (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)
- [Open-World Human-Object Interaction Detection via Multi-modal Prompts](https://mlsanity.com/paper/2406.07221/) (2024)
- [Neural-Logic Human-Object Interaction Detection](https://mlsanity.com/paper/2311.09817/) (2023)
- [Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics](https://mlsanity.com/paper/2202.00259/) (2022)
- [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)
- [Sequential Voting with Relational Box Fields for Active Object Detection](https://mlsanity.com/paper/2110.11524/) (2021)
- [Unified Visual Relationship Detection with Vision and Language Models](https://mlsanity.com/paper/2303.08998/) (2023)
- [Locality-Aware Zero-Shot Human-Object Interaction Detection](https://mlsanity.com/paper/2505.19503/) (2025)
- [Highlighting Object Category Immunity for the Generalization of Human-Object Interaction Detection](https://mlsanity.com/paper/2202.09492/) (2022)
- [GTNet:Guided Transformer Network for Detecting Human-Object Interactions](https://mlsanity.com/paper/2108.00596/) (2021)
- [Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration](https://mlsanity.com/paper/2508.03207/) (2025)
- [Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions](https://mlsanity.com/paper/2211.07501/) (2022)
- [Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning](https://mlsanity.com/paper/2403.10107/) (2024)
- [A Review of Human-Object Interaction Detection](https://mlsanity.com/paper/2408.10641/) (2024)
- [An Image-like Diffusion Method for Human-Object Interaction Detection](https://mlsanity.com/paper/2503.18134/) (2025)
- [Learning Human-Object Interaction as Groups](https://mlsanity.com/paper/2510.18357/) (2025)
- [UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection](https://mlsanity.com/paper/2408.07430/) (2024)
- [DDS: Decoupled Dynamic Scene-Graph Generation Network](https://mlsanity.com/paper/2301.07666/) (2023)
- [Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models](https://mlsanity.com/paper/2311.16475/) (2023)
- [No More Sibling Rivalry: Debiasing Human-Object Interaction Detection](https://mlsanity.com/paper/2509.00760/) (2025)
- [Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO](https://mlsanity.com/paper/2201.02396/) (2022)
- [Parallel Reasoning Network for Human-Object Interaction Detection](https://mlsanity.com/paper/2301.03510/) (2023)
- [Towards Flexible Visual Relationship Segmentation](https://mlsanity.com/paper/2408.08305/) (2024)
- [Knowledge Guided Bidirectional Attention Network for Human-Object Interaction Detection](https://mlsanity.com/paper/2207.07979/) (2022)
- [SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data](https://mlsanity.com/paper/2308.12910/) (2023)

## Abstract (arXiv)

Recent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection. To tackle the problems above, we propose a novel one-stage HOI detection approach DIRV in this paper, based on a new concept called interaction region for the HOI problem. Unlike previous methods, our approach concentrates on the densely sampled interaction regions across different scales for each human-object pair, so as to capture the subtle visual features that is most essential to the interaction. Moreover, in order to compensate for the detection flaws of a single interaction region, we introduce a novel voting strategy that makes full use of those overlapped interaction regions in place of conventional Non-Maximal Suppression (NMS). Extensive experiments on two popular benchmarks: V-COCO and HICO-DET show that our approach outperforms existing state-of-the-arts by a large margin with the highest inference speed and lightest network architecture. We achieved 56.1 mAP on V-COCO without addtional input. Our code is publicly available at: https://github.com/MVIG-SJTU/DIRV