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dc.contributor.authorChao, Yu-Wei
dc.contributor.authorLiu, Yunfan
dc.contributor.authorLiu, Xieyang
dc.contributor.authorZeng, Huayi
dc.contributor.authorDeng, Jia
dc.date.accessioned2018-01-04T07:51:41Z
dc.date.available2018-01-04T07:51:41Z
dc.date.issued2018-05-07
dc.identifier.citationChao, Y.-W., Liu, Y., Liu, X., Zeng, H., & Deng, J. (2018). Learning to Detect Human-Object Interactions. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2018.00048
dc.identifier.doi10.1109/wacv.2018.00048
dc.identifier.urihttp://hdl.handle.net/10754/626708
dc.description.abstractWe study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2015-CRG4-2639.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8354152/
dc.rightsArchived with thanks to IEEE
dc.titleLearning to Detect Human-Object Interactions
dc.typeConference Paper
dc.conference.date2018-03-12 to 2018-03-15
dc.conference.name18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
dc.conference.locationLake Tahoe, NV, USA
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Michigan, Ann Arbor, United States
dc.contributor.institutionWashington University in St. Louis, United States
dc.identifier.arxivid1702.05448
kaust.grant.numberOSR-2015-CRG4-2639
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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