An improved helmet detection method for YOLOv3 on an unbalanced dataset

基于注意力增强网络与增强泛化性能的改进YOLO安全帽检测

Better YOLO with Attention-Augmented Network and Enhanced Generalization Performance for Safety Helmet Detection

本文提出一种基于GhostNetv2轻量骨干、SCNet与CANet注意力模块及GAM优化器的改进YOLO安全帽检测方法，在减少25%以上参数和计算量的同时将mAP提升2%，兼顾精度、泛化与效率。

Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution

Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. Several techniques were proposed to improve the accuracy of BNNs. One of the approaches is to balance the distribution of binary activations so that the amount of information in the binary activations becomes maximum. Based on extensive analysis, in stark contrast to previous work…

Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network

For many years, the emotion recognition task has remained one of the most interesting and important problems in the field of human-computer interaction. In this study, we consider the emotion recognition task as a classification as well as a regression task by processing encoded emotions in different datasets using deep learning models. Our model combines convolutional neural network (CNN) with recurrent neural network (RNN) to predict dimensional emotions on video data. At the first step, CNN extracts feature vectors from video frames. In the second step, we fed these feature vectors to train…

CT中改进通用病灶检测与标记的类别不平衡校正

Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT

本文针对DeepLesion数据集的类别不平衡问题，通过按身体部位、患者病灶数和病灶大小平衡数据训练VFNet等模型，提升了CT病灶检测与标记的敏感度和召回率。