In Defense of Scene Graphs for Image Captioning

Kien Nguyen, Subarna Tripathi, Bang Du, Tanaya Guha, Truong Q. Nguyen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1407-1416

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 naive use of scene graphs from a black-box scene graph generator harms image captioning performance, and 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 a framework, SG2Caps, that utilizes only the scene graph labels for competitive image captioning performance. The basic idea is to close the semantic gap between two scene graphs - one derived from the input image and the other one 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. Our framework outperforms existing scene graph-only captioning models by a large margin indicating scene graphs as a promising representation for image captioning. Direct utilization of the scene graph labels avoids expensive graph convolutions over high-dimensional CNN features resulting in 49% fewer trainable parameters. The code is available at: https://github.com/Kien085/SG2Caps.