# Transferable Interactiveness Knowledge for Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/1811.08264 · HTML: https://mlsanity.com/paper/1811.08264/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [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)
- [Detecting Human-Object Interaction via Fabricated Compositional Learning](https://mlsanity.com/paper/2103.08214/) (2021)
- [Affordance Transfer Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2104.02867/) (2021)
- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Discovering Human-Object Interaction Concepts via Self-Compositional Learning](https://mlsanity.com/paper/2203.14272/) (2022)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning](https://mlsanity.com/paper/2303.01313/) (2023)
- [Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels](https://mlsanity.com/paper/2309.05069/) (2023)
- [A Global to Local Double Embedding Method for Multi-person Pose Estimation](https://mlsanity.com/paper/2102.07318/) (2021)

## Abstract (arXiv)

Human-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates whether human and object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets, regardless of HOI category settings. Our core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference. On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We extensively evaluate the proposed method on HICO-DET and V-COCO datasets. Our framework outperforms state-of-the-art HOI detection results by a great margin, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.