A Review of Human-Object Interaction Detection

TL;DR

This paper reviews recent advances in human-object interaction detection, covering datasets, methods, and emerging trends like zero-shot learning and large-scale language models, highlighting current challenges and future directions.

Contribution

It provides a comprehensive summary and analysis of recent developments in image-based HOI detection, including datasets, methodologies, and new research trends.

Findings

Analysis of two-stage and end-to-end detection methods

Discussion on zero-shot and weakly supervised learning in HOI

Exploration of large-scale language models in HOI detection

Abstract

Human-object interaction (HOI) detection plays a key role in high-level visual understanding, facilitating a deep comprehension of human activities. Specifically, HOI detection aims to locate the humans and objects involved in interactions within images or videos and classify the specific interactions between them. The success of this task is influenced by several key factors, including the accurate localization of human and object instances, as well as the correct classification of object categories and interaction relationships. This paper systematically summarizes and discusses the recent work in image-based HOI detection. First, the mainstream datasets involved in HOI relationship detection are introduced. Furthermore, starting with two-stage methods and end-to-end one-stage detection approaches, this paper comprehensively discusses the current developments in image-based HOI detection, analyzing the strengths and weaknesses of these two methods. Additionally, the advancements of zero-shot learning, weakly supervised learning, and the application of large-scale language models in HOI detection are discussed. Finally, the current challenges in HOI detection are outlined, and potential research directions and future trends are explored.