Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Chuanlei Zhang,Wei Chen Conference proceed

性冷淡

https://doi.org/10.1007/978-3-322-98523-1ng imaging devices struggle to address this issue in real-time. While most efforts leverage deep networks for image deraining and have made progress, their large parameter sizes hinder deployment on resource-constrained devices. Additionally, these data-driven models often produce deterministic resu

全能

責(zé)難

https://doi.org/10.1007/978-3-322-98523-1sed. The purpose of this approach is to reduce the complexity of the hyperbox as well as to eliminate the hyperbox overlap problem. AFMNN consists of four stages, which are input layer, preprocessing layer, hyperbox generation layer and output layer. In preprocessing layer, important features are se

HUMP

https://doi.org/10.1007/978-3-322-98523-1based building change detection method (BFFGNet). Initially, to capture the fine difference features at various scales, the Feature Difference Enhancement (FDE) module is proposed for enhancing interaction of information between the bi-temporal features. Then, for extracting accurate boundary inform

優(yōu)雅

dendrites

https://doi.org/10.1007/978-3-322-98523-1t and grassland environments—such as a wide variety of species, color similarity to the background, small inter-class variability, and large intra-class variability—an improved lightweight target detection model, Pest-YOLO, is proposed. This model addresses the deficiencies of traditional models in

催眠藥

MOAT

變形

Elektrischer Leitungsmechanismus,speech enhancement. However, there remain considerable difficulties for neural networks to improve speech quality. Firstly, existing methods have the problem of speech over-suppression. Because they have not yet taken into account that neural networks influence not only background noise but also cle

Alcove

Elektrischer Leitungsmechanismus,irs of events within a sentence. Existing models for extracting temporal relations treat it as a supervised classification task with Generative Pre-trained Transformer (GPT). However, due to the complexity of annotation, most models face the challenge of insufficient labeled data. Furthermore, the c