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Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks. [ABSTRACT FROM AUTHOR]

Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also compressing the model for lightweight deployment. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale offline handwritten signature dataset, including genuine signatures and forged signatures in Chinese, Uyghur, Kazakh, and Kirgiz, totaling 38,400 images. Our FC-ResNet model achieved an accuracy of over 96% for each language in our self-built dataset, as well as accuracy rates of 96.21%, 98.42%, and 97.28% on the public datasets CEDAR, BHSig-B, and BHSig-H, respectively. Based on the above experimental results, our proposed model demonstrates great potential for both public and self-built signature datasets, while also exhibiting significant advantages in lightweight model deployment. We believe that this work can provide a feasible solution for ethnic people signature verification. [ABSTRACT FROM AUTHOR]

Implicit sentiment identification is a significant classical task in text analysis. Graph neural networks (GNNs) have recently been successful in implicit sentiment identification, but the current approaches still suffer from two problems. On the one hand, there is a lack of structural information carried by the single-view graph structure of implicit sentiment texts to accurately capture obscure sentiment expressions. On the other hand, the predefined fixed graph structure may contain some noisy edges that cannot represent semantic information using an accurate topology, which can seriously impair the performance of implicit sentiment analysis. To address these problems, we introduce a knowledge-fusion-based iterative graph structure learning framework (KIG). Specifically, for the first problem, KIG constructs graph structures based on three views, namely, co-occurrence statistics, cosine similarity, and syntactic dependency trees through prior knowledge, which provides rich multi-source information for implicit sentiment analysis and facilitates the capture of implicit obscure sentiment expressions. To address the second problem, KIG innovatively iterates the three original graph structures and searches for their implicit graph structures to better fit the data themselves to optimize the downstream implicit sentiment analysis task. We compared our method with the mainstream implicit sentiment identification methods on two publicly available datasets, and ours outperformed both benchmark models. The accuracy, recall, and F1 values of KIG on the Pun of the Day dataset reached 89.2%, 93.7%, and 91.1%, respectively. Extensive experimental results demonstrate the superiority of our proposed method for the implicit sentiment identification task. [ABSTRACT FROM AUTHOR]

In multilingual scene text understanding, script identification is an important prerequisite step for text image recognition. Due to the complex background of text images in natural scenes, severe noise, and common symbols or similar layouts in different language families, the problem of script identification has not been solved. This paper proposes a new script identification method based on ConvNext improvement, namely EA-ConvNext. Firstly, the method of generating an edge flow map from the original image is proposed, which increases the number of scripts and reduces background noise. Then, based on the feature information extracted by the convolutional neural network ConvNeXt, a coordinate attention module is proposed to enhance the description of spatial position feature information in the vertical direction. The public dataset SIW-13 has been expanded, and the Uyghur script image dataset has been added, named SIW-14. The improved method achieved identification rates of 97.3%, 93.5%, and 92.4% on public script identification datasets CVSI-2015, MLe2e, and SIW-13, respectively, and 92.0% on the expanded dataset SIW-14, verifying the superiority of this method. [ABSTRACT FROM AUTHOR]

Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a new Res2Net-based improved script identification method, namely FAS-Res2Net. In the feature extraction part, the feature pyramid network (FPN) module is introduced, which is beneficial to aggregate the geometric feature information extracted by the shallow network and the semantic feature information extracted by the deep network. Integrating the Adaptive Spatial Feature Fusion (ASFF) module is beneficial to obtain local feature information for optimal weight fusion. In addition, the global feature information of the image is extracted by introducing the swin transformer coding block, which makes the extracted feature information more abundant. In the classification part, the convolutional classifier is used to replace the traditional Linear classification, and the classification confidence of each category is output, which improves the identification efficiency. The improved algorithm achieved identification rates of 94.7% and 96.0% on public script identification datasets SIW-13 and CVSI-2015, respectively, which verified the superiority of the method. [ABSTRACT FROM AUTHOR]

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]