Sequential latent spaces for modeling the intention during diverse image captioning

Jyoti Aneja, Harsh Agrawal, Dhruv Batra, Alexander Schwing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. In common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t. sentence quality.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4260-4269
Number of pages10
ISBN (Electronic)9781728148038
DOIs
StatePublished - Oct 2019
Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
Duration: Oct 27 2019Nov 2 2019

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2019-October
ISSN (Print)1550-5499

Conference

Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
CountryKorea, Republic of
CitySeoul
Period10/27/1911/2/19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Aneja, J., Agrawal, H., Batra, D., & Schwing, A. (2019). Sequential latent spaces for modeling the intention during diverse image captioning. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 4260-4269). [9010960] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00436