ALDI-ray: Adapting the ALDI Framework for Security X-ray Object Detection

arXiv — cs.LGWednesday, December 3, 2025 at 5:00:00 AM
  • The ALDI++ framework has been adapted for security X-ray object detection, addressing the challenges posed by domain adaptation in real-world applications. This adaptation is crucial due to the significant variations in scanning devices and environmental conditions that can degrade model performance, as demonstrated through extensive experiments on the EDS dataset.
  • The implementation of ALDI++ has shown to surpass state-of-the-art domain adaptation methods, particularly with its Vision Transformer backbone, achieving the highest mean average precision. This advancement is significant for enhancing the reliability and accuracy of security X-ray imaging systems.
  • The development of ALDI++ reflects a broader trend in artificial intelligence where hybrid architectures, such as those integrating Vision Transformers, are increasingly utilized across various applications, including medical imaging and disaster assessment. This shift highlights the growing importance of robust domain adaptation techniques in improving model performance across diverse scenarios.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps
Continue Readings
First On-Orbit Demonstration of a Geospatial Foundation Model
PositiveArtificial Intelligence
The first on-orbit demonstration of a Geospatial Foundation Model (GeoFM) has been successfully conducted aboard the International Space Station, showcasing compact variants of a Vision Transformer that maintain performance while being resource-efficient. This advancement addresses the challenges posed by the large size of traditional GeoFMs, which hinder their deployment in space environments.
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer
PositiveArtificial Intelligence
A novel training algorithm called Forward-Forward has been introduced for Vision Transformers, aiming to emulate brain-like processing by placing loss functions after each layer and using two local forward passes along with one backward pass. This approach, still in its early stages, seeks to address performance gaps compared to traditional backpropagation methods.