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1. Attention-augmented domain-adaptive semantic segmentation of remote sensing images | |||
XUE Bingjie,CAI Minjie | |||
Computer Science and Technology 28 April 2022 | |||
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Abstract:In order to achieve semantic segmentation of unlabeled images using labeled remote sensing image datasets, an unsupervised domain adaptive method is proposed in this paper to solve this problem. Firstly, a self-training network for semantic segmentation of remote sensing images based on pseudo labels selection is established. Secondly, in view of the shortcoming that convolution network model can\'t effectively utilize long-range dependence information, attention module is added to the segmentation network part to effectively utilize the relationship between image dependence information and channels. Finally, in order to learn domain invariant features, a domain classifier is added on the basis of the self-training network, which ensures that the domain classifier can\'t distinguish which domain the sample comes from while ensuring the segmentation performance, thus reducing the domain offset between the source domain and the target domain. Experiments on LoveDA dataset show that this method is superior to the mainstream unsupervised domain adaptive method, and achieves good results in unsupervised domain adaptive semantic segmentation of remote sensing images in rural and urban areas. Exploring the deep transfer learning method on this dataset will be a meaningful way to promote large-scale land-cover mapping. | |||
TO cite this article:XUE Bingjie,CAI Minjie. Attention-augmented domain-adaptive semantic segmentation of remote sensing images[OL].[28 April 2022] http://en.paper.edu.cn/en_releasepaper/content/4757582 |
2. Design and Implementation of Handwritten Chinese Characters Recognition Based on Deep Learning | |||
Jingyi Shen | |||
Computer Science and Technology 24 March 2022 | |||
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Abstract:Nowadays, people are increasingly using electronic formats to store data. In order to convert the table in paper into digital information, this project implements a handwritten Chinese character recognition system for table pictures, which releases the pressure of manual workload of typing handwritten Chinese character information. This project pre-processes the scanned images, extracts the handwritten text information from the images . Then segment the cell in the table and cuts the individual Chinese characters. In order to improve the accuracy of character cutting, this project adopts the combination of vertical projection and aspect ratio of character to determine the cutting position. The ResNext50 model is used for model training, and the two models are trained to recognize numbers, letters and handwritten Chinese characters respectively. The accuracy of the Chinese character recognition model is more than 90%, and that of the number and letter recognition model is 98%. Based on the contents filled in the table, the list of proper nouns is used to correct the recognition results and improve the accuracy. By calculating Levenshtein distance find the specific nouns with the highest similarity. The method proposed in this paper effectively complete the separation and recognition of handwritten Chinese characters in the table image. | |||
TO cite this article:Jingyi Shen. Design and Implementation of Handwritten Chinese Characters Recognition Based on Deep Learning[OL].[24 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4756976 |
3. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid | |||
LI Ning,LI Liqun | |||
Computer Science and Technology 23 March 2022 | |||
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Abstract:Aiming at the loss of high-frequency information components of visible light and infrared images fused by Latent Low-Rank Representation (LatLRR) algorithm, this paper proposes a multi-level image fusion algorithm based on the combination of LatLRR and Gauss-Laplace Pyramid. Firstly, the image is decomposed into Gaussian pyramid and Laplace pyramid, then the highest level image of Gaussian pyramid and each layer image of Laplace pyramid are decomposed into low-rank part and Significant part, meanwhile the low-rank part and significant part of infrared and visible image pyramid are blended, then the fused low-rank part and significant part are fused at all levels, and finally the image is restored by Gaussian-Laplace pyramid. The algorithm has been tested on the public data set, this method retains more high-frequency information components compared with LatLRR algorithm. | |||
TO cite this article:LI Ning,LI Liqun. Fusion algorithm of infrared and visible images based on LatLRR and image pyramid[OL].[23 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4757034 |
4. Feature selection scheme based on firefly algorithm | |||
Meng Hao-tian,Peng Hai-peng | |||
Computer Science and Technology 07 March 2022 | |||
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Abstract:In intrusion detection, processing high-dimensional data is often an important step. High-dimensional data brings higher computing cost and more difficult to process. Therefore, it is essential to set up dimensionality reduction scheme. Aiming at the problems of easy convergence and high computational cost of the traditional firefly algorithm, this paper proposes a feature selection scheme based on the firefly algorithm by improving the firefly algorithm. The specific improvement methods are as follows: for the initial population, define a vector composed of 0-1 for a single firefly individual, and set a one-to-one correspondence for discrete features; For the step size, the dynamic update step size mechanism is introduced, and the initial step size is set to a higher value, which can better global search in the early stage of the algorithm iteration. After a certain number of iterations, the step size gradually decreases, and then quickly converges to the optimal solution; For the brightness function and selection mode, the distance between classes is introduced as the brightness function to redefine the selection mode. The improved firefly algorithm selects the features of KDDCUP99 data set and obtains 10 optimal features, which are \{3,4,5,6,8,10,13,23,24,37\}. After training the 10 features, the accuracy is 99.48\%. | |||
TO cite this article:Meng Hao-tian,Peng Hai-peng. Feature selection scheme based on firefly algorithm[OL].[ 7 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4756684 |
5. WeSSRD: A weakly supervised app store spam reviews detection framework | |||
LI Siyi,XU Guosheng,LIN Yan,Guo yanhui,Xu Guoai | |||
Computer Science and Technology 23 February 2022 | |||
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Abstract:With the popularity of smartphones, a large number of apps have emerged in app store for users to download.Most app stores allow users who download an app to post reviews and ratings on this app. These reviews are not only a major factor in determining the ranking of an app, but also a major reference for users in choosing whether to download the app, and an important way for developers to get feedback from users.However, a large number of meaningless reviews (or called spam reviews) have severely damaged the normal ecology of the app store and are one of the urgent problems to be solved in maintaining the regular order of the mobile app market. This paper proposes a weakly supervised spam detection framework called WeSSRD. It can mine app reviews for relevance to the app itself by unsupervised topic modeling methods and then train a weakly supervised detector to detect spam in application stores using a minimal amount of prior knowledge.We tested this framework on a real dataset with 14,052 reviews. The detector trained by our proposed framework can achieve a precision rate of 80.97% and a recall rate of 81.89% on the test set, far exceeding the detection method based on similarity. | |||
TO cite this article:LI Siyi,XU Guosheng,LIN Yan, et al. WeSSRD: A weakly supervised app store spam reviews detection framework[OL].[23 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756424 |
6. THMobile : An Improved Network For Garbage Classification Based on MobileNet | |||
Zhou Jialan,Bian Jiali | |||
Computer Science and Technology 22 February 2022 | |||
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Abstract:With the rapid development of deep learning, more and more image recognition models are applied to daily life. For the current neural network model, the recognition accuracy of large model is higher and higher, but the more resources are needed. The lightweight of neural network model is more conducive to the application in life. In this paper, a THMobile model with smaller size and higher accuracy is proposed based on MobileNet. On the self-made garbage dataset, the classification accuracy of it reaches 91.2%, obtaining better performance than MobileNet. And it also performs better on CIFAR-10 than MobileNet. | |||
TO cite this article:Zhou Jialan,Bian Jiali. THMobile : An Improved Network For Garbage Classification Based on MobileNet[OL].[22 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756338 |
7. Efficient video transmission scheme based on deep compressed sensing | |||
YANG Zihang,LI Lixiang | |||
Computer Science and Technology 21 February 2022 | |||
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Abstract:With the rapid development of wireless communication technology and the popularity of mobile video input devices, wireless video transmission technology has been widely used in intelligent transportation, intelligent industry, intelligent security and other fields, which also brings many security problems. In this paper, a secure, fast and efficient video transmission scheme (SFE-VTS) for video sensor network is designed by combining deep compressed sensing and adaptive video codec technology. In the coding end, the new adaptive selection algorithm of sampling position and sampling rate of video frame block reduces the amount of data transmitted by redundant information and solves the problem of fluctuation of recovery quality between adjacent non-key frames. In the decoding side, the recovery algorithm based on deep learning can reconstruct the video frame quickly. In this paper, the proposed scheme can effectively solve the traditional video transmission scheme, encoding end takes up too much resource and transport process safety is not high. In addition, compared with some video transmission schemes based on traditional compressed sensing, the recovery effect and efficiency are higher. | |||
TO cite this article:YANG Zihang,LI Lixiang. Efficient video transmission scheme based on deep compressed sensing[OL].[21 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756385 |
8. A Long-Distance Respiratory Monitoring Based on Milimeter Wave Sensing | |||
YU Shuhui,ZHOU Anfu | |||
Computer Science and Technology 18 February 2022 | |||
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Abstract:Vital sign monitoring is a key technology to ensure human health. Respiration, as an important sign of human health, has become an important research direction. Current breathing monitoring methods are generally divided into contact monitoring based on wearable devices and non-contact monitoring based on wireless signal measurement. The former requires the user to actively wear it, which is not suitable for long-term monitoring; the latter can only achieve breathing monitoring within a close range of 1-1.5m due to signal attenuation . To solve this problem, this paper proposes a long- distance breathing monitoring system based on multi-channel millimeter wave signals . When realizing long-distance breathing monitoring, for the two major challenges of the significant energy attenuation and directivity reduction of millimeter wave signals when they propagate over long distances, we targeted the use of a multi-channel antenna array to enhance the energy of the effective signal, and use the smallest Variance-free distortion response (MVDR) adaptively accurately locates the thoracic cavity from multiple signal sources. Subsequently, the principal component analysis ( PCA) was used to extract the principal components of the chest motion, and finally the respiratory rate was calculated based on the phase slope. We evaluate the accuracy of sitting and lying in different postures in daily life. Experimental results show that we can achieve an average error of 0.6 BPM in the 2.5m. | |||
TO cite this article:YU Shuhui,ZHOU Anfu. A Long-Distance Respiratory Monitoring Based on Milimeter Wave Sensing[OL].[18 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756246 |
9. Compressive Hyperspectral Video Reconstruction Via Multitask Nonparametric Bayesian Learning | |||
Yang Man,Gao Zhanchun | |||
Computer Science and Technology 09 February 2022 | |||
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Abstract:Compressive hyperspectral video reconstruction (CHVR) extends snapshot spectral imaging into the temporal dimension, which allows fast hyerspectral observation of dynamic scenes. This paper proposes a multitask learning method for CHVR under the blind compressive sensing framework, characterized by joint inference of the representation atoms and the corresponding coefficients, directly from the compressive measurements. Defining the compressive reconstruction of each frame as a single task, our method employs a common dictionary shared by all tasks, which significantly alleviates the data paucity problem. The complete inference process is fulfilled via a Bayesian nonparametric estimation strategy, which contributes three advantages: reliable generalizability, parameter-tuning free and automatic determination of the model complexity. Simulation results demonstrate the efficacy of the proposed approach. | |||
TO cite this article:Yang Man,Gao Zhanchun. Compressive Hyperspectral Video Reconstruction Via Multitask Nonparametric Bayesian Learning[OL].[ 9 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756225 |
10. A region-based error concealment method for livestream videos | |||
Wang Mengyuan,Wang Jing | |||
Computer Science and Technology 29 January 2022 | |||
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Abstract:This paper proposes a video error concealment method for the reconstruction of large size corrupted regions of livestream videos. The method adopts different spatial and temporal error concealment methods according to the characteristics of livestream frames and can keep a well balance on time-cost and performance. First, the method named motion vector estimation is performed for small motion regions. Then it uses the improved adaptive homography transform to reconstruct unknown regions in each frame. Finally, a spatial error concealment method known as bilinear interpolation is performed to conceal the macroblocks with poor effect after above two steps. Experiment results show that the proposal has the same performance as the baseline paper for livestream videos, and it is less time-consuming which can be applied in real livestream applications. | |||
TO cite this article:Wang Mengyuan,Wang Jing. A region-based error concealment method for livestream videos[OL].[29 January 2022] http://en.paper.edu.cn/en_releasepaper/content/4756191 |
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