# Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/2401.05676 · HTML: https://mlsanity.com/paper/2401.05676/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 39 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 30 of 32)

- [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)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (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)
- [HOTR: End-to-End Human-Object Interaction Detection with Transformers](https://mlsanity.com/paper/2104.13682/) (2021)
- [Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP](https://mlsanity.com/paper/2109.02748/) (2021)
- [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)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [DRG: Dual Relation Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2008.11714/) (2020)
- [Learning Human-Object Interaction Detection using Interaction Points](https://mlsanity.com/paper/2003.14023/) (2020)
- [Reformulating HOI Detection as Adaptive Set Prediction](https://mlsanity.com/paper/2103.05983/) (2021)
- [Spatially Conditioned Graphs for Detecting Human-Object Interactions](https://mlsanity.com/paper/2012.06060/) (2020)
- [Detecting Human-Object Interactions with Action Co-occurrence Priors](https://mlsanity.com/paper/2007.08728/) (2020)
- [Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection](https://mlsanity.com/paper/2104.05269/) (2021)
- [Mining Cross-Person Cues for Body-Part Interactiveness Learning in HOI Detection](https://mlsanity.com/paper/2207.14192/) (2022)
- [Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries](https://mlsanity.com/paper/2105.02170/) (2021)
- [UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection](https://mlsanity.com/paper/2312.12664/) (2023)
- [Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics](https://mlsanity.com/paper/2202.00259/) (2022)
- [Category Query Learning for Human-Object Interaction Classification](https://mlsanity.com/paper/2303.14005/) (2023)
- [Improving Human-Object Interaction Detection via Phrase Learning and Label Composition](https://mlsanity.com/paper/2112.07383/) (2021)

## Built on by (most cited first, 7 of 7)

- [Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration](https://mlsanity.com/paper/2508.03207/) (2025)
- [Learning Human-Object Interaction as Groups](https://mlsanity.com/paper/2510.18357/) (2025)
- [ContextHOI: Spatial Context Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2412.09050/) (2024)
- [Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache](https://mlsanity.com/paper/2511.18811/) (2025)
- [Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection](https://mlsanity.com/paper/2604.02071/) (2026)
- [Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild](https://mlsanity.com/paper/2607.13881/) (2026)
- [Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection](https://mlsanity.com/paper/2604.18313/) (2026)

## Abstract (arXiv)

Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods mainly extract multi-modal features (e.g., appearance, object semantics, human pose) and then fuse them together to directly predict HOI triplets. However, most of these methods focus on seeking for self-triplet aggregation, but ignore the potential cross-triplet dependencies, resulting in ambiguity of action prediction. In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection. Specifically, we regard each triplet proposal as a graph where Human, Object represent nodes and Action indicates edge, to aggregate self-triplet correlation. Also, we try to explore cross-triplet dependencies by jointly considering instance-level, semantic-level, and layout-level relations. Besides, we leverage the CLIP model to assist our SCTC obtain interaction-aware feature by knowledge distillation, which provides useful action clues for HOI detection. Extensive experiments on HICO-DET and V-COCO datasets verify the effectiveness of our proposed SCTC.