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Found 9 papers, 5 papers with code
Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
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).
Examining Monitoring System: Detecting Abnormal Behavior In Online Examinations
Cheating in online exams has become a prevalent issue over the past decade, especially during the COVID-19 pandemic.
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection.
Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
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.
A Novel Explainable Artificial Intelligence Model in Image Classification problem
Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM.
G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors
Nowadays, deep neural networks for object detection in images are very prevalent.
Towards Better Explanations for Object Detection
Recent advances in Artificial Intelligence (AI) technology have promoted their use in almost every field.
Towards Trust of Explainable AI in Thyroid Nodule Diagnosis
In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application.
