2 citations · 6 across the 5 of their papers we have counts for
5 papers
Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen +3
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 Expl…
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
Truong Thanh Hung Nguyen, Tobias Clement, Phuc Truong Loc Nguyen +5
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite…
Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Quoc Hung Cao +3
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. Our research pr…
G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors
Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen +2
Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objec…
Towards Trust of Explainable AI in Thyroid Nodule Diagnosis
Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen +2
The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-ma…