MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Center for Brains, Minds & Machines
  • Publications
  • CBMM Memo Series
  • View Item
  • DSpace@MIT Home
  • Center for Brains, Minds & Machines
  • Publications
  • CBMM Memo Series
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Recurrent Multimodal Interaction for Referring Image Segmentation

Author(s)
Liu, Chenxi; Lin, Zhe; Shen, Xiaohui; Yang, Jimei; Lu, Xin; Yuille, Alan L.; ... Show more Show less
Thumbnail
DownloadCBMM-Memo-079.pdf (10.15Mb)
Metadata
Show full item record
Abstract
In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently and then segment images by combining these two types of representations. We argue that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and we propose convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information. We show that our proposed model outperforms the baseline model on benchmark datasets. In addition, we analyze the intermediate output of the proposed multimodal LSTM approach and empirically explain how this approach enforces a more effective word-to-image interaction.
Date issued
2018-05-10
URI
http://hdl.handle.net/1721.1/115374
Publisher
Center for Brains, Minds and Machines (CBMM)
Series/Report no.
CBMM Memo Series;079

Collections
  • CBMM Memo Series

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.