# Distance Matters in Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/2207.01869 · HTML: https://mlsanity.com/paper/2207.01869/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 59 references that are in the map.
- First posted on arXiv 5 Jul 2022.

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

- [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)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [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)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers](https://mlsanity.com/paper/2106.05392/) (2021)
- [cosFormer: Rethinking Softmax in Attention](https://mlsanity.com/paper/2202.08791/) (2022)
- [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)
- [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)
- [RegionViT: Regional-to-Local Attention for Vision Transformers](https://mlsanity.com/paper/2106.02689/) (2021)
- [DRG: Dual Relation Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2008.11714/) (2020)
- [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://mlsanity.com/paper/1609.06647/) (2016)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection](https://mlsanity.com/paper/2107.06592/) (2021)
- [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)

## Built on by (most cited first, 8 of 8)

- [Unseen No More: Unlocking the Potential of CLIP for Generative Zero-shot HOI Detection](https://mlsanity.com/paper/2408.05974/) (2024)
- [GPT4Ego: Unleashing the Potential of Pre-trained Models for Zero-Shot Egocentric Action Recognition](https://mlsanity.com/paper/2401.10039/) (2024)
- [Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration](https://mlsanity.com/paper/2508.03207/) (2025)
- [A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap](https://mlsanity.com/paper/2407.21438/) (2024)
- [Improving Human-Object Interaction Detection via Virtual Image Learning](https://mlsanity.com/paper/2308.02606/) (2023)
- [Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild](https://mlsanity.com/paper/2607.13881/) (2026)
- [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)

## Abstract (arXiv)

Human-Object Interaction (HOI) detection has received considerable attention in the context of scene understanding. Despite the growing progress on benchmarks, we realize that existing methods often perform unsatisfactorily on distant interactions, where the leading causes are two-fold: 1) Distant interactions are by nature more difficult to recognize than close ones. A natural scene often involves multiple humans and objects with intricate spatial relations, making the interaction recognition for distant human-object largely affected by complex visual context. 2) Insufficient number of distant interactions in benchmark datasets results in under-fitting on these instances. To address these problems, in this paper, we propose a novel two-stage method for better handling distant interactions in HOI detection. One essential component in our method is a novel Far Near Distance Attention module. It enables information propagation between humans and objects, whereby the spatial distance is skillfully taken into consideration. Besides, we devise a novel Distance-Aware loss function which leads the model to focus more on distant yet rare interactions. We conduct extensive experiments on two challenging datasets - HICO-DET and V-COCO. The results demonstrate that the proposed method can surpass existing approaches by a large margin, resulting in new state-of-the-art performance.