The development of deep learning-based biometric models that can be deployed on devices
with constrained memory and computational resources has proven to be a significant …

At present, deep learning has been widely used in SAR ship target detection, but the
accurate and real-time detection of multi-scale targets still faces tough challenges. CNN …

The global and local contexts significantly contribute to the integrity of predictions in Salient
Object Detection (SOD). Unfortunately, existing methods still struggle to generate complete …

Contour information plays a vital role in salient object detection. However, excessive false
positives remain in predictions from existing contour-based models due to insufficient …

Salient Object Detection (SOD) is dominated by Encoder-Decoder networks which involve
multi-scale feature fusion and multi-resolution dense supervision. It is prevalent yet …

In the spirit of innovation, the development of an intelligent robot system incorporating the
basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an …

The conventional reconstruction method of off-axis digital holographic microscopy (DHM)
relies on computational processing that involves spatial filtering of the sample spectrum and …

Temporal knowledge graph (TKG) reasoning, which aims to extrapolate missing facts in
TKGs, is vital for many significant applications, such as event prediction. Previous studies …

Named entity recognition is a technique for extracting named entities from text and
classifying them into various entity types. There has been a lot of research done on the …

High-precision localization and machine learning (ML) are envisioned to be key
technologies in future wireless systems. This paper presents an ML pipeline to solve …