# Interact as You Intend: Intention-Driven Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/1808.09796 · HTML: https://mlsanity.com/paper/1808.09796/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

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

- [QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information](https://mlsanity.com/paper/2103.05399/) (2021)
- [Affordance Transfer Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2104.02867/) (2021)
- [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021)
- [Context Understanding in Computer Vision: A Survey](https://mlsanity.com/paper/2302.05011/) (2023)
- [Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition](https://mlsanity.com/paper/2108.04603/) (2021)
- [Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows](https://mlsanity.com/paper/2203.10537/) (2022)
- [Video-based Human-Object Interaction Detection from Tubelet Tokens](https://mlsanity.com/paper/2206.01908/) (2022)
- [Human-Object Interaction Prediction in Videos through Gaze Following](https://mlsanity.com/paper/2306.03597/) (2023)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection](https://mlsanity.com/paper/2109.04047/) (2021)
- [Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection](https://mlsanity.com/paper/2207.02400/) (2022)
- [HODN: Disentangling Human-Object Feature for HOI Detection](https://mlsanity.com/paper/2308.10158/) (2023)
- [Distance Matters in Human-Object Interaction Detection](https://mlsanity.com/paper/2207.01869/) (2022)
- [Agglomerative Transformer for Human-Object Interaction Detection](https://mlsanity.com/paper/2308.08370/) (2023)
- [Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition](https://mlsanity.com/paper/2404.11903/) (2024)
- [Joint Gaze-Location and Gaze-Object Detection](https://mlsanity.com/paper/2308.13857/) (2023)
- [Learning Human-Human Interactions in Images from Weak Textual Supervision](https://mlsanity.com/paper/2304.14104/) (2023)
- [RoHOI: Robustness Benchmark for Human-Object Interaction Detection](https://mlsanity.com/paper/2507.09111/) (2025)
- [Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning](https://mlsanity.com/paper/2211.16264/) (2022)
- [Geometric Features Enhanced Human-Object Interaction Detection](https://mlsanity.com/paper/2406.18691/) (2024)
- [RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection](https://mlsanity.com/paper/2104.15015/) (2021)
- [Distillation of Human-Object Interaction Contexts for Action Recognition](https://mlsanity.com/paper/2112.09448/) (2021)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [Learning to Minimize the Remainder in Supervised Learning](https://mlsanity.com/paper/2201.09193/) (2022)
- [Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection](https://mlsanity.com/paper/2307.10499/) (2023)
- [On the Robustness of Human-Object Interaction Detection against Distribution Shift](https://mlsanity.com/paper/2506.18021/) (2025)
- [Funnel-HOI: Top-Down Perception for Zero-Shot HOI Detection](https://mlsanity.com/paper/2507.12628/) (2025)
- [Multimodal Graph Network Modeling for Human-Object Interaction Detection with PDE Graph Diffusion](https://mlsanity.com/paper/2509.12554/) (2025)
- [Where are they looking in the operating room?](https://mlsanity.com/paper/2604.20574/) (2026)
- [InterMesh: Explicit Interaction-Aware End-to-End Multi-Person Human Mesh Recovery](https://mlsanity.com/paper/2605.04554/) (2026)

## Abstract (arXiv)

The recent advances in instance-level detection tasks lay strong foundation for genuine comprehension of the visual scenes. However, the ability to fully comprehend a social scene is still in its preliminary stage. In this work, we focus on detecting human-object interactions (HOIs) in social scene images, which is demanding in terms of research and increasingly useful for practical applications. To undertake social tasks interacting with objects, humans direct their attention and move their body based on their intention. Based on this observation, we provide a unique computational perspective to explore human intention in HOI detection. Specifically, the proposed human intention-driven HOI detection (iHOI) framework models human pose with the relative distances from body joints to the object instances. It also utilizes human gaze to guide the attended contextual regions in a weakly-supervised setting. In addition, we propose a hard negative sampling strategy to address the problem of mis-grouping. We perform extensive experiments on two benchmark datasets, namely V-COCO and HICO-DET. The efficacy of each proposed component has also been validated.