{"type":"ScholarlyArticle","title":"LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks","authors":[{"name":"Hung Nguyen","slug":"hung-nguyen","given":"Hung","family":"Nguyen","affiliation":"Analytics Everywhere Lab , University of New Brunswick , Canada; Quy Nhon AI , FPT Software , Vietnam"},{"name":"Tobias Clement","slug":"tobias-clement","given":"Tobias","family":"Clement","affiliation":"Friedrich-Alexander-University Erlangen- Nürnberg , Germany"},{"name":"Loc Nguyen","slug":"loc-nguyen","given":"Loc","family":"Nguyen","affiliation":"Friedrich-Alexander-University Erlangen- Nürnberg , Germany"},{"name":"Nils Kemmerzell","slug":"nils-kemmerzell","given":"Nils","family":"Kemmerzell","affiliation":"Friedrich-Alexander-University Erlangen- Nürnberg , Germany"},{"name":"Binh Truong","slug":"binh-truong","given":"Binh","family":"Truong","affiliation":"Quy Nhon AI , FPT Software , Vietnam"},{"name":"Khang Nguyen","slug":"khang-nguyen","given":"Khang","family":"Nguyen","affiliation":"Quy Nhon AI , FPT Software , Vietnam"},{"name":"Mohamed Abdelaal","slug":"mohamed-abdelaal","given":"Mohamed","family":"Abdelaal","affiliation":"Software AG , Germany"},{"name":"Hung Cao","slug":"hung-cao","given":"Hung","family":"Cao","affiliation":"Analytics Everywhere Lab , University of New Brunswick , Canada"}],"abstract":"LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite XAI advancements, an understanding gap persists for end-users with limited domain knowledge in artificial intelligence and computer vision. LangXAI addresses this by furnishing text-based explanations for classification, object detection, and semantic segmentation model outputs to end-users. Preliminary results demonstrate LangXAI's enhanced plausibility, with high BERTScore across tasks, fostering a more transparent and reliable AI framework on vision tasks for end-users. The code and demo of this work can be found at https://analytics-everywhere-lab.github.io/langxai.io/.","doi":"10.24963/ijcai.2024/1025","url":"https://ijcai.org/proceedings/2024/1025","pdf":"https://ijcai.org/proceedings/2024/1025.pdf","markdown":"https://ijcai.org/proceedings/2024/1025.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1025","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1025,"pages":{"first":8754,"last":8758},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Abdelfattah et al., 2020] Rabab Abdelfattah, Xiaofeng Wang, and Song Wang. 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