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Target tracking algorithms based on deep learning have achieved good results in public datasets. Among them, the network tracking algorithm based on Siamese tracking has a high accuracy and fast speed, which has attracted significant attention. However, the Siamese tracker uses the AlexNet network as its backbone and the network layers are relatively shallow, so it does not make full use of the ability of the deep neural network. If only the backbones of target tracking are replaced, there will be no obvious improvement, such as in the cases of ResNet and Inception. Therefore, this paper designs a wider and deeper network structure. At a wider level, a mechanism that can adaptively adjust the receptive field (RF) size is designed. Firstly, multiple branches are divided by the split operator, and each branch has a different size of kernel corresponding to a different size of RF; then, the fuse operator is used to fuse the information of each branch to obtain the selection weights; and finally, according to the selection, the aggregation feature map is weighted. At a deeper level, a new kind of residual models is designed. The channel is simplified by pruning in order to improve the tracking speed. According to the above, a wider and deeper Siamese network was proposed in this paper. The experimental results show that the structure proposed in this paper achieves a good tracking effect and real-time performance on six kinds of datasets. The proposed tracker achieves an SUC and Prec of LaSOT of 0.569 and 0.571, respectively. [ABSTRACT FROM AUTHOR]

As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classification; this method is named Text To Picture (TTP) in this paper. TTP highlights the sentiment differences between different sentiment polarities in Chinese implicit sentiment text with the help of deep learning by converting original text data into word frequency maps. The differences between sentiment polarities are used as sentiment clues to improve the performance of the Chinese implicit sentiment text classification task. It does this by transforming the original text data into a word frequency map in order to highlight the differences between the sentiment polarities expressed in the implicit sentiment text. We conducted experimental tests on two common datasets (SMP2019, EWECT), and the results show that the accuracy of our method is significantly improved compared with that of the competitor's. On the SMP2019 dataset, the accuracy-improvement range was 4.55–7.06%. On the EWECT dataset, the accuracy was improved by 1.81–3.95%. In conclusion, the new preprocessing method for implicit sentiment text classification proposed in this paper can achieve better classification results. [ABSTRACT FROM AUTHOR]

The current most advanced CNN-based detection models are nearly not deployable on mobile devices with limited arithmetic power due to problems such as too many redundant parameters and excessive arithmetic power required, and knowledge distillation as a potentially practical model compression approach can alleviate this limitation. In the past, feature-based knowledge distillation algorithms focused more on transferring the local features customized by people and reduced the full grasp of global information in images. To address the shortcomings of traditional feature distillation algorithms, we first improve GAMAttention to learn the global feature representation in images, and the improved attention mechanism can minimize the information loss caused by processing features. Secondly, feature shifting no longer defines manually which features should be shifted, a more interpretable approach is proposed where the student network learns to emulate the high-response feature regions predicted by the teacher network, which increases the end-to-end properties of the model, and feature shifting allows the student network to simulate the teacher network in generating semantically strong feature maps to improve the detection performance of the small model. To avoid learning too many noisy features when learning background features, these two parts of feature distillation are assigned different weights. Finally, logical distillation is performed on the prediction heads of the student and teacher networks. In this experiment, we chose Yolov5 as the base network structure for teacher–student pairs. We improved Yolov5s through attention and knowledge distillation, ultimately achieving a 1.3% performance gain on VOC and a 1.8% performance gain on KITTI. • We use knowledge distillation and attention for yolov5s to improve performance. • We propose the GAMF module that captures both global and local features. • We propose the RFT and join SSIM to shift response features and background features. [ABSTRACT FROM AUTHOR]