In the process of printed document image retrieval, the traditional algorithm, SURF algorithm combined with violent matching, has the problems of low retrieval accuracy and low retrieval efficiency. This paper propose...
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ISBN:
(纸本)9798400707988
In the process of printed document image retrieval, the traditional algorithm, SURF algorithm combined with violent matching, has the problems of low retrieval accuracy and low retrieval efficiency. This paper proposes a FAST + PCA-SURF combined with KNN algorithm based on FLANN for multilingual document image retrieval. Based on FAST, the feature points are detected, and the feature descriptors after dimensionality reduction are extracted by ***, the KNN algorithm based on FLANN is used for feature matching, and finally the appropriate matching results are output. The experimental results show that the proposed algorithm is improved in terms of time complexity and retrieval accuracy compared with the traditional algorithm. The average time complexity and retrieval accuracy of the traditional algorithm are 0.1783 s and 71.8%, respectively, while the proposed algorithm is 0.0464 s and 77.8%, indicating that the proposed algorithm achieves better experimental results in multilingual document image retrieval.
China is a major industrial country in the world. In various construction environments, the falling of construction materials and collisions on construction sites are the main causes of casualties. Accidents caused by...
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Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real...
Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.
Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for s...
Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for semi-supervised SED based on Mean Teacher (MT) method. For teacher and student models, each adopts two branches with the same CRNN structure, the two branches help training the model by means of consistency regularization. ISCT strategy incorporates self-consistency loss on the basis of MT loss to improve the generalization performance of the model. A selective feature fusion (SFF) module is designed for applying in the shallow layers of the feature extraction part to selectively fuse the features with different scales. A parallel attention (PA) module is designed for applying in the deep layers of the feature extraction part to obtain much richer high-level features by the channel and spatial-wise attention. Ablation experiments verify the effectiveness of our proposed ISCT strategy, SFF and PA modules. In addition, compared with four methods, our proposed method achieves competitive performance on the DCASE 2020 task4 dataset.
In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time ser...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets.
作者：
Meng, ShanAblimit, MijitHamdulla, AskarXinjiang University
Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang Urumqi China Xinjiang University
Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang Urumqi China
Voice activity detection (VAD) is an important preprocessing for voice applications. Anti-noise performance is the most important evaluation index of VAD algorithm. The traditional dual-threshold-based VAD algorithm h...
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Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Sch...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Schemes (or Ponzi Contracts) and have caused huge financial losses. Current Ponzi contract detection models face three main challenges: simple opcode sequence processing does not effectively distinguish Ponzi from non-Ponzi contracts, single-feature-based models lack accuracy, and reliance on transaction records hinders early detection. To address these issues, this paper proposes a Multi-Feature Ponzi Scheme detection Model (MFDPonzi). MFDPonzi tracks the changes in stack, memory, and storage parameters during the execution of smart contracts, reconstructing opcode sequences and extracting diverse features, including semantic and developer features. Finally, a multi-feature fusion algorithm is used to enhance model stability. Additionally, MFDPonzi can identify Ponzi contracts at the early stage of smart contract creation without relying on transaction data. Experimental results show that MFDPonzi achieves an 85.9% recall and an 88.7% F-score on Ethereum smart contracts, outperforming baselines in both performance and robustness.
With the development of artificial intelligence, obtaining textual information from natural scenes has become a hot topic. There are still huge challenges for curved text and arbitrary orientation text detection in re...
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In recent years, there has been rapid development in deep learning-based End-To-End speech recognition technology. However, the performance of Turkish speech recognition systems has been hindered by the lack of Turkis...
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Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then f...
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