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Found 17 papers, 6 papers with code
MLA-Net: Feature Pyramid Network with Multi-Level Local Attention for Object Detection
Abstract: Feature pyramid networks and attention mechanisms are the mainstream methods to improve the detection performance of many current models.
Using the Choquet Integral in the Pooling Layer in Deep Learning Networks
The Choquet integral is an aggregation function studied and applied in several areas, as, e. g., in classification problems.
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91. 5% and 93. 0% for tooth and alveolar bone segmentation.
Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma
We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction.
High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning
The results show that, the proposed method can well suppress the noise and signal distortion caused by the laser frequency drift, laser phase noise, and interference fading, while recover the acoustic signals with high fidelity.
InfoGAN-MSF: a data augmentation approach for correlative bridge monitoring factors
Simulation results imply that the proposed model performs effectively in data generation of real-world bridge monitoring factors and improves the performance of bridge health evaluation.
An Efficient Privacy-Preserving Multi-Keyword Query Scheme in Location Based Services
In this article, aiming at a multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the data users.
Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band
Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance.
Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck
Abstract: Road boundary detection is an important part of the perception of autonomous driving.
SIAMESE NETWORK BASED METRIC LEARNING FOR SAR TARGET CLASSIFICATION
Different from classification networks that predict the category of one sample, the Siamese network implements a metric learning to measure the similarity between two samples.
Deep Transfer Learning for Few-Shot SAR Image Classification
Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data.
A Single Attention-Based Combination of CNN and RNN for Relation Classification
This network structure utilizes RNN to extract higher level contextual representations of words and CNN to obtain sentence features for the relation classification task.
Deep Video Frame Interpolation using Cyclic Frame Generation
In addition to the cycle consistency loss, we propose two extensions: motion linearity loss and edge-guided training.
GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction
Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network.
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
The output of the three components are weighted fused to generate the final prediction results.
Ranked #14 on
Traffic Prediction
on PeMS07
Welding Defect Classification Based on Convolution Neural Network (CNN) and Gaussian Kernel
Results of the classification used to get the category of weld defects with high accuracy as a variable of a weld inspection process whether the weld is pass the standard or not.
结合遗传算法的LVQ神经网络 在声学底质分类中的应用
学习向量量化(Learning Vector Quantization, LVQ)神经网络在声学底质分类中具有广泛应用. 常用的LVQ 神经网络存在神经元未被充分利用以及算法对初值敏感的问题, 影响底质分类精度. 本文提出采用遗传算法 (Genetic Algorithms, GA)优化神经网络的初始值, 将GA与LVQ神经网络结合起来, 迅速得到最佳的神经网络初始权 值向量, 实现对海底基岩、砾石、砂、细砂以及泥等底质类型的快速、准确识别. 将其应用于青岛胶州湾海区底质分
