LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
Hung Nguyen, Tobias Clement, Loc Nguyen, Nils Kemmerzell, Binh Truong, Khang Nguyen, Mohamed Abdelaal, Hung Cao
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/.
BibTeX
@inproceedings{ijcai2024p1025,
title = {LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks},
author = {Nguyen, Hung and Clement, Tobias and Nguyen, Loc and Kemmerzell, Nils and Truong, Binh and Nguyen, Khang and Abdelaal, Mohamed and Cao, Hung},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8754--8758},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1025},
url = {https://doi.org/10.24963/ijcai.2024/1025},
}