LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
LangXAI is a framework that integrates Explain able Artificial Intelligence (XAI) with advanced vi sion models to generate textual explanations for visual recognition tasks. Despite XAI advance ments, an understanding gap persists for end-users with limited domain knowledge in artificial intel ligence and computer vision. LangXAI addresses this by furnishing text-based explanations for clas sification, object detection, and semantic segmen tation model outputs to end-users. Preliminary re sults demonstrate LangXAI’s enhanced plausibil ity, with high BERTScore across tasks, fostering a more transparent and reliable AI framework on vi sion tasks for end-users
Research paper: 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
3/21/2025
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To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME).
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The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug.
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In recent years, reinforcement learning (RL) has emerged as a new, promising way to solve old problems. The algorithms’ role in finding approximate solutions in NP-hard complexity became crucial for developing modern intelligent decisions.
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