{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sentistory-a-multi-layered-sentiment-aware","title":"SentiStory: A Multi-Layered Sentiment-Aware Generative Model for Visual Storytelling","arxiv_id":null,"date":"2022-06-16","proceeding":"IEEE Transactions on Circuits and Systems for Video Technology 2022 6","authors":["Wei Chen","Xuefeng Liu","Jianwei Niu"],"abstract":"The visual storytelling (VIST) task aims at generating reasonable, human-like and coherent stories with the image streams as input. Although many deep learning models have achieved promising results, most of them do not directly leverage the sentiment information of stories. In this paper, we propose a sentiment-aware generative model for VIST called SentiStory. The key of SentiStory is a multi-layered sentiment extraction module (MLSEM). For a given image stream, the higher layer gives coarse-grained but accurate sentiments, while the lower layer of the MLSEM extracts fine-grained but usually unreliable ones. The two layers are combined strategically to generate coherent and rich visual sentiment concepts for the VIST task. Results from both automatic and human evaluations demonstrate that with the help of the MLSEM, SentiStory achieves improvement in generating more coherent and human-like stories.","url_abs":"https://ieeexplore.ieee.org/document/9797749","url_pdf":"https://ieeexplore.ieee.org/document/9797749","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"visual-storytelling","task_name":"Visual Storytelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-storytelling-on-vist","task":"Visual Storytelling","dataset":"VIST","model":"SentiStory","rank_in_archive_order":5,"of":33,"metrics":{"BLEU-1":"65.5","BLEU-2":"40.7","BLEU-3":"24.1","BLEU-4":"14.8","CIDEr":"10.1","METEOR":"35.7","ROUGE-L":"30.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}