In Defense of Scene Graphs for Image Captioning

Nguyen, K., Tripathi, S., Du, B., Guha, T. and Nguyen, T. Q. (2021) In Defense of Scene Graphs for Image Captioning. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10-17 October 2021, pp. 1387-1396. ISBN 9781665428125 (doi: 10.1109/ICCV48922.2021.00144)

Abstract

The mainstream image captioning models rely on Convolutional Neural Network (CNN) image features to generate captions via recurrent models. Recently, image scene graphs have been used to augment captioning models so as to leverage their structural semantics, such as object entities, relationships and attributes. Several studies have noted that the naive use of scene graphs from a black-box scene graph generator harms image captioning performance and that scene graph-based captioning models have to incur the overhead of explicit use of image features to generate decent captions. Addressing these challenges, we propose SG2Caps, a framework that utilizes only the scene graph labels for competitive image captioning performance. The basic idea is to close the semantic gap between the two scene graphs - one derived from the input image and the other from its caption. In order to achieve this, we leverage the spatial location of objects and the Human-Object-Interaction (HOI) labels as an additional HOI graph. SG2Caps outperforms existing scene graph-only captioning models by a large margin, indicating scene graphs as a promising representation for image captioning. Direct utilization of scene graph labels avoids expensive graph convolutions over high-dimensional CNN features resulting in 49% fewer trainable parameters. Our code is available at: https://github.com/Kien085/SG2Caps.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Guha, Dr Tanaya
• Authors: Nguyen, K., Tripathi, S., Du, B., Guha, T., and Nguyen, T. Q.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2380-7504
• ISBN: 9781665428125
• Published Online: 28 February 2021
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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