HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales

• URL: http://arxiv.org/abs/2302.12189v3
• Date: Mon, 25 Sep 2023 07:37:20 GMT
• Title: HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales
• Authors: Michele Cafagna, Kees van Deemter, Albert Gatt
• Abstract summary: We present a dataset extending 14997 images from the COCO dataset, aligned with a new set of 134,973 human-annotated (high-level) captions. We further extend this dataset with confidence scores collected from an independent set of readers, as well as a set of narrative captions generated synthetically.
• Score: 5.010418546872244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current captioning datasets focus on object-centric captions, describing the visible objects in the image, e.g. "people eating food in a park". Although these datasets are useful to evaluate the ability of Vision & Language models to recognize and describe visual content, they do not support controlled experiments involving model testing or fine-tuning, with more high-level captions, which humans find easy and natural to produce. For example, people often describe images based on the type of scene they depict ('people at a holiday resort') and the actions they perform ('people having a picnic'). Such descriptions draw on personal experience and commonsense assumptions. We present the High-Level Dataset a dataset extending 14997 images from the COCO dataset, aligned with a new set of 134,973 human-annotated (high-level) captions collected along three axes: scenes, actions, and rationales. We further extend this dataset with confidence scores collected from an independent set of readers, as well as a set of narrative captions generated synthetically, by combining each of the three axes. We describe this dataset and analyse it extensively. We also present baseline results for the High-Level Captioning task.

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