Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916926249238528 |
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| author | Oliveira, Daniel A. P. Ribeiro, Eugénio de Matos, David Martins |
| author_facet | Oliveira, Daniel A. P. Ribeiro, Eugénio de Matos, David Martins |
| contents | Creating engaging narratives from visual data is crucial for automated digital media consumption, assistive technologies, and interactive entertainment. This survey covers methodologies used in the generation of these narratives, focusing on their principles, strengths, and limitations.
The survey also covers tasks related to automatic story generation, such as image and video captioning, and visual question answering, as well as story generation without visual inputs. These tasks share common challenges with visual story generation and have served as inspiration for the techniques used in the field. We analyze the main datasets and evaluation metrics, providing a critical perspective on their limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges Oliveira, Daniel A. P. Ribeiro, Eugénio de Matos, David Martins Computer Vision and Pattern Recognition Artificial Intelligence I.2.7; I.2.10 Creating engaging narratives from visual data is crucial for automated digital media consumption, assistive technologies, and interactive entertainment. This survey covers methodologies used in the generation of these narratives, focusing on their principles, strengths, and limitations. The survey also covers tasks related to automatic story generation, such as image and video captioning, and visual question answering, as well as story generation without visual inputs. These tasks share common challenges with visual story generation and have served as inspiration for the techniques used in the field. We analyze the main datasets and evaluation metrics, providing a critical perspective on their limitations. |
| title | Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.7; I.2.10 |
| url | https://arxiv.org/abs/2406.02748 |