Image classification and captioning model considering a CAM-based disagreement loss

Yeo Chan Yoon, So Young Park, Soo Myoung Park, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

Original languageEnglish
Pages (from-to)67-77
Number of pages11
JournalETRI Journal
Issue number1
Publication statusPublished - 2020 Feb 1

Bibliographical note

Publisher Copyright:
© 2019 ETRI


  • deep learning
  • image captioning
  • image classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Computer Science
  • Electrical and Electronic Engineering


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