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Cited by 74Multimodal Machine Learning ApplicationsAdvanced Neural Network ApplicationsImage Retrieval and Classification Techniques

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Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning

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Dong, Xinzhi, Long, Chengjiang, Xu, Wenju, Xiao, Chunxia·2021·Proceedings of the 29th ACM International Conference on Multimedia
DOI 10.1145/3474085.3475439
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Abstract

Existing image captioning methods just focus on understanding the relationship between objects or instances in a single image, without exploring the contextual correlation existed among contextual image. In this paper, we propose Dual Graph Convolutional Networks (Dual-GCN) with transformer and curriculum learning for image captioning. In particular, we not only use an object-level GCN to capture the object to object spatial relation within a single image, but also adopt an image-level GCN to capture the feature information provided by similar images. With the well-designed Dual-GCN, we can make the linguistic transformer better understand the relationship between different objects in a single image and make full use of similar images as auxiliary information to generate a reasonable caption description for a single image. Meanwhile, with a cross-review strategy introduced to determine difficulty levels, we adopt curriculum learning as the training strategy to increase the robustness and generalization of our proposed model. We conduct extensive experiments on the large-scale MS COCO dataset, and the experimental results powerfully demonstrate that our proposed method outperforms recent state-of-the-art approaches. It achieves a BLEU-1 score of 82.2 and a BLEU-2 score of 67.6. Our source code is available at https://github.com/Unbear430/DGCN-for-image-captioning.

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Metadata

Authors
Dong, X., Long, C., Xu, W., & Xiao, C.
Affiliations
  • Wuhan University (CN)
  • JD Finance America Corporation, Mountain View, CA, USA
  • InnoPeak Technology Inc., Palo Alto, CA, USA
ORCID
Venue
Proceedings of the 29th ACM International Conference on Multimedia
Publisher
ACM
Published
2021-10-17
Pages
2615-2624
DOI
10.1145/3474085.3475439
OpenAlex
W3205071568
Cited by
74
References
8
Citations by year
2026: 3 · 2025: 6 · 2024: 15 · 2023: 27 · 2022: 13 · 2021: 10
FWCI
5.32
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM International Conference on Multimedia (ACM MM) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
NSFC (61972298)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
image captioning, graph convolutional networks, transformer models, curriculum learning, MS COCO

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