Visual Semantic Relatedness Dataset for Image Captioning

Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 5598-5606

Abstract

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story. In this paper, we propose a textual visual context dataset for captioning, in which the publicly available dataset COCO Captions (Lin et al., 2014) has been extended with information about the scene (such as objects in the image). Since this information has a textual form, it can be used to leverage any NLP task, such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.

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[bibtex]
@InProceedings{Sabir_2023_CVPR, author = {Sabir, Ahmed and Moreno-Noguer, Francesc and Padr\'o, Llu{\'\i}s}, title = {Visual Semantic Relatedness Dataset for Image Captioning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {5598-5606} }