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). Our method efficiently generates concise saliency maps by utilizing activation maps from selected layers and applying a Gaussian kernel to emphasize critical image regions for the predicted object. Compared with other Region-based approaches, G-CAME significantly reduces explanation time to 0.5 seconds without compromising the quality. Our evaluation of G-CAME, using Faster-RCNN and YOLOX on the MS-COCO2017dataset, demonstrates its ability to offer highly plausible and faithful explanations, especially in reducing the bias on tiny object detection.
Research paper: Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
Khanh Nguyen, Hung Nguyen, Khang Nguyen, Binh Truong, Tuong Phan, Hung Cao
3/21/2025
NEW
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.
NEW
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.
QaiDora Products
QaiDora draws inspiration from the myth of Pandora’s box—a symbol of unexpected possibilities and hope. For us, AI models are like modern Pandora’s boxes, holding untapped potential to turn challenges into opportunities. At QAI, QaiDora serves as an ecosystem of AI products designed to drive innovation and deliver competitive advantages.
Trusted by
Contact us
