# Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

arXiv: https://arxiv.org/abs/1903.06355 · HTML: https://mlsanity.com/paper/1903.06355/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Reformulating HOI Detection as Adaptive Set Prediction](https://mlsanity.com/paper/2103.05983/) (2021)
- [Human-centric Relation Segmentation: Dataset and Solution](https://mlsanity.com/paper/2105.11168/) (2021)
- [Effective Actor-centric Human-object Interaction Detection](https://mlsanity.com/paper/2202.11998/) (2022)
- [Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection](https://mlsanity.com/paper/2307.10499/) (2023)
- [A Turbo-Inference Strategy for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2606.12371/) (2026)

## Abstract (arXiv)

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects' localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.