Detecting human-object interaction with multi-level pairwise feature network  

作　　者：Hanchao Liu Tai-Jiang Mu Xiaolei Huan 

机构地区：[1]Key Laboratory of Pervasive Computing,Ministry of Education,BNRist,Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China [2]College of Information Sciences and Technology,Pennsylvania State University,University Park,PA 16802,USA.

出　　处：《Computational Visual Media》2021年第2期229-239,共11页计算可视媒体（英文版）

基　　金：supported by the National Natural Science Foundation of China(Project No.61902210),a Research Grant of Beijing Higher Institution Engineering Research Center,and the Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology.

摘　　要：Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an image.Recent studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the pair.We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the action.We thus propose a multi-level pairwise feature network(PFNet)for detecting human–object interactions.The network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action prediction.Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

关 键 词：human–object interaction detection pairwise feature network deep learning MULTI-LEVEL object instance 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]