LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks

TL;DR

LangXAI is a framework that combines large vision models with explainable AI techniques to produce textual explanations, making visual perception tasks more transparent and understandable for users with limited AI knowledge.

Contribution

It introduces a novel approach that integrates vision models with language-based explanations, improving interpretability in visual recognition tasks.

Findings

High BERTScore indicating plausible explanations

Enhanced transparency and trust in vision models

Effective in classification, detection, and segmentation tasks

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.