Titlebook: Artificial Intelligence in China; Proceedings of the 3 Qilian Liang,Wei Wang,Zhenyu Na Conference proceedings 2022 The Editor(s) (if applic

障碍物

Reverse Attention U-Net for Brain Grey Matter Nuclei Segmentation,ns while highlighting background, which guides the network to explore the missing nuclei parts sequentially. Experimental results on our nuclei dataset imply that the RAU-Net performs favorably against the state-of-the-art methods.

有权威

食道

Small-Object Detection with Super Resolution Embedding,f-the-art detector (YOLOv3). Extensive experiments on a public (car overhead with context) dataset and another self-assembled airport surface dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

contradict

Ringworm

Autoencoder-Based Baseline Parameterized by Central Limit Theorem for ICS Cybersecurity,d has stable interactive features. In the paper, we analyze the ICS network interaction and construct a parameterized baseline by an autoencoder to detect the intrusion. The experiment with an open ICS dataset shows that this baseline could achieve intrusion detection accuracy above 90% and the false alarm rate below 5%.

熔岩

Image Compression Based on Mixed Matrix Decomposition of NMF and SVD,work on images. The experimental results demenstrated that this approach based on mixed matrix decomposition had a CR with larger dynamic range through flexible parameter adjustment and the PSNR of the restored image is 29 dB–36 dB. It verifiy that this method is effective.

Peculate

Information Extraction of Air-Traffic Control Instructions via Pre-trained Models,ts of handcraft annotations. The large scale pre-trained model (PTMs) can solve this problem by “pre-training” and “fine-tuning”. This paper proposes: 1) pre-trained models to extract information from few scale ATC instructions; 2) the probing task to find which layer of model achieves the best performance of information extraction task.

ethereal

Medical Image Segmentation Using Transformer,amed TransHarDNet. HarDNet, which is a low memory traffic CNN. We combine it as backbone with Transformer. Our network enables the global semantic context information and low-level spatial details of the input image to be captured more effectively. We evaluate the effectiveness of the proposed network on five medical image datasets.

大气层

myalgia