Transferable Interactiveness Prior for Human-Object Interaction Detection.
Year: 2018
Type: preprint
Abstract: Human-Object Interaction (HOI) Detection is an important problem tounderstand how humans interact with objects. In this paper, we explore
\textbf{Interactiveness Prior} which indicates whether human and object
interact with each other or not. We found that interactiveness prior 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
prior from multiple HOI datasets and perform Non-Interaction Suppression before
HOI classification in inference. On account of the generalization of
interactiveness prior, 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. Source codes
and models will be made publicly available.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2901532862