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In order to solve the problem that the Transformer model cannot apply to mobile devices with limited computational resources due to its huge number of parameters and computational complexity, this paper proposed a mobile-friendly lightweight image recognition network called FuseNet. FuseNet utilized convolutional neural network to extract local feature information and self-attention to excel in modeling global information, and it integrated the features of both local and global representations into a single feature extraction module, which efficiently combined the advantages of the two different structures to achieve a high accuracy with a small model size. Experiments demonstrate that FuseNet with different model sizes can achieve good performance without using pre-training and it can well apply to mobile devices. FuseNet-B achieves 80. 5% accuracy with 14. 8 M parameters on ImageNet-1 K dataset, the performance exceeds the same volume of Transformer models and convolutional neural networks. [ABSTRACT FROM AUTHOR]

Most of the eye disease classification methods are to classify a single category of diseases at different levels, and the network model has problems such as large parameters and complex calculations. To solve these problems, this paper proposed a lightweight fundus disease multi-classification network called MELCNet. The network used PPLCNet as the backbone, which was composed of input layer for feature extraction, parallel multi-scale structure, bilinear SE attention module, depth separable convolution and the h-swish activation function which calculated with smaller parameters, in order to pay attention to the key disease information of different scales and different diseases. Experimental results show that the proposed multi-scale attention lightweight network model has less parameters and computational complexity, and excellent classification results have been obtained in the multi-classification of the four selected fundus diseases and normal fundus images. The classification accuracy in the internal combined test set is 1.11 % higher than that in ResNet-50,and the performance on the public datasets is improved by 2. 5% relative to the multi-classification literature of similar type network by Xie et al, and compared with other lightweight classification networks, it has higher accuracy and strong robustness in the field of multi-classification of fund us diseases. [ABSTRACT FROM AUTHOR]

To solve the problem of category-oriented 3D articulated objects pose estimation, this paper proposed a category-oriented 3D object pose estimation method based on key points. It designed a network based on key points, which used PointNet + + as base network and estimated the pose of the object through feature extraction, key points extraction, and pose prediction with high accuracy and robust. At the same time, based on ANCSH, it proposed a hierarchical standardized category representation method of key points for K-AOPE, which only required a few points to describe the object category. To evaluate the method, it did some experiments on the public motion dataset shape2motion. The results show that the rotation angle errors of the proposed method are 2. 3°, 3. 1°, 3. 7°, the translation errors are 0. 034, 0. 030, 0. 046, the joint state error are 2. 4°, 2. 5°, and the joint parameter error are 1. 2°, 0. 9°, 0. 008, 0. 010. Compared with other similar methods, the proposed method has lower computational complexity and higher robust. [ABSTRACT FROM AUTHOR]