ViP-CNN: Visual Phrase Guided Convolutional Neural Network
Refereed conference paper presented and published in conference proceedings


Other information
AbstractAs the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.
Acceptance Date21/07/2017
All Author(s) ListYikang Li, Wanli Ouyang, Xiaogang Wang, Xiao'ou Tang
Name of Conference2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference21/07/2017
End Date of Conference26/07/2017
Place of ConferenceHonolulu, Hawaii
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017
Year2017
Pages7244 - 7253
ISBN978-1-5386-0457-1
LanguagesEnglish-United States

Last updated on 2018-04-05 at 15:07