mlsanityData to 14 Oct 2026

A Review of Human-Object Interaction Detection

arXiv 2408.10641 ↗First posted 20 Aug 2024cs.CV cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 59 of its 74 references that are in the map. First posted on arXiv 20 Aug 2024.

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59 of 74 references.

References · 59

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38 more
  1. 2021End-to-End Human Object Interaction Detection with HOI Transformercited by 135
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