Extract Contextual Information Research Articles

Landslide extraction from aerial imagery considering context association characteristics

Accurate extraction of landslide information is crucial for timely disaster emergency response, yet this process faces significant challenges due to the interference of bare land and vegetation cover as well as the variability in landslide scales. Furthermore, the scarcity of comprehensive landslide datasets has hindered the in-depth exploration of deep learning techniques in this domain. Addressing these issues, this study introduces a novel framework for landslide extraction that leverages the characteristics of context association. In the encoding phase, a two-branch multiscale context feature extraction module (TMCFM) is established, which captures the contextual relationships across different scales through an attention mechanism while concurrently extracting context information within the same scale. To refine the interplay between features at different levels, self-attention is utilized, allowing for the effective fusion of context information at both ends of the same scale through feature fusion exploiting static and dynamic context feature modules (FSDC). Building upon this, this study develops a deeply supervised classifier (DSC) that enhances the network’s discriminative capabilities in the prediction phase via six auxiliary branches. Additionally, this study contributes a new aerial image dataset for landslide extraction created through meticulous visual interpretation. The proposed method is thoroughly compared with 17 contemporary deep learning methods, demonstrating an increase in the intersection-over-union (IoU) metric of 0.92%-16.94% over these methods. The robustness and superiority of the proposed approach are further validated through various discussions and analyses.

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

Complete contextual information extraction for self-supervised monocular depth estimation

Self-supervised learning methods are increasingly important for monocular depth estimation since they do not require ground-truth data during training. Although existing methods have achieved great success for better monocular depth estimation based on Convolutional Neural Networks (CNNs), the limited receptive field of CNNs usually is insufficient to effectively model the global information, e.g., relationship between foreground and background or relationship among objects, which are crucial for accurately capturing scene structure. Recently, some studies based on Transformers have attracted significant interest in computer vision. However, duo to the lack of spatial locality bias, they may fail to model the local information, e.g., fine-grained details with an image. To tackle these issues, we propose a novel self-supervised learning framework by incorporating the advantages of both the CNNs and Transformers so as to model the complete contextual information for high-quality monocular depth estimation. Specifically, the proposed method mainly includes two branches, where the Transformer branch is considered to capture the global information while the Convolution branch is exploited to preserve the local information. We also design a rectangle convolution module with pyramid structure to perceive the semi-global information, e.g. thin objects, along the horizontal and vertical directions within an image. Moreover, we propose a shape refinement module by learning the affinity matrix between pixel and its neighborhood to obtain accurate geometrical structure of scenes. Extensive experiments evaluated on KITTI, Cityscapes and Make3D dataset demonstrate that the proposed method achieves the competitive result compared with the state-of-the-art self-supervised monocular depth estimation methods and shows good cross-dataset generalization ability.

Optimizing Insulator Defect Detection with Improved DETR Models

With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using Detection Transformer (DETR). In this study, we propose a multi-scale backbone network that effectively captures the features of small objects, enhancing the detection performance. Additionally, we introduce a self-attention upsampling (SAU) module to replace the conventional attention module, enhancing contextual information extraction and facilitating the detection of small objects. Furthermore, we introduce the insulator defect (IDIoU) loss, which mitigates the instability in the matching process caused by small defects. Extensive experiments were conducted on an insulator defect dataset to evaluate the performance of our proposed method. The results demonstrate that our approach achieves outstanding performance, particularly in detecting small defects. Compared to existing methods, our approach exhibits a remarkable 7.47% increase in the average precision, emphasizing its efficacy in insulator defect detection. The proposed method not only enhances the accuracy of defect detection, which is crucial for maintaining the reliability and safety of power transmission systems but also has broader implications for the maintenance and inspection of high-voltage power infrastructure.

Speech Keyword Spotting Method Based on Swin-Transformer Model

With the rapid advancements in deep learning technology, the Transformer-based attention neural network has shown promising performance in keyword spotting (KWS). However, this method suffers from high computational cost since the excessive parameters in the Transformer model and the computational burden of global attention, which limit its applicability in a resource-constrained KWS scenario. To overcome this issue, we propose a novel Swin-Transformer based KWS method. In this approach, first extract dynamic features using Temporal Convolutional Network (TCN) from input Mel-Frequency Cepstral Coefficients (MFCCs). Then, the Swin-Transformer is employed to capture hierarchical multi-scale features, where a window attention is designed to grasp dynamic time–frequency features. Furthermore, to enhance the extraction of contextual information from the spectrogram, a frame-level shifted window attention mechanism is proposed to enhance the inter-window interaction, thus extracting more contextual information from the spectrogram. Experimental results on the speech command V1 dataset verify the effectiveness of the proposal, which achieves a recognition accuracy of 98.01% with less model parameters, outperforming existing KWS methods.

A method for multi-target segmentation of bud-stage apple trees based on improved YOLOv8

The bud stage is a crucial period in the growth and development of apple trees. Accurate detections of the physiological changes in the organs (branches, buds, leaves, and the connecting parts between buds and branches) are essential for the scientific management of orchards. In the field of intelligent orchard management, image segmentation is a fundamental method for obtaining the phenotypes of fruit tree organs, making it especially critical. To address this, we have created a dataset for apple tree organ segmentation during the bud stage and have incorporated several advanced convolutional network modules (ConvNeXt V2, Multi-scale Extended Attention Module (MSDA), Dynamic Snake Convolution (DSConv)) to enhance YOLOv8 and improve the accuracy of organ segmentation in complex natural environments. In the backbone network, we have integrated ConvNeXt V2 and MSDA modules to increase the extraction of contextual information and improve the network’s ability to recognize multi-scale and multi-shaped targets. Additionally, we have embedded DSConv in the network’s head and utilized deformable convolution to enable adaptive sampling of the feature map, capturing a wider range of context information and improving the local feature processing capability for objects of varying sizes, ultimately leading to improved segmentation accuracy. Our models significantly outperform existing models, achieving 82.58 % mean Precision (mP), 74.58 % mean Recall (mR), 77.94 % mean Dice(mDice), 64.91 % mean IoU(mIoU), and 79.75 % mean Average Precision (mAP). Ablation studies confirm the contributions of each module to intelligent orchard management and suggest potential benefits for precise agricultural decision-making and operations.

Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4

Abstract N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts. Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3528.

FGMNet: Feature grouping mechanism network for RGB-D indoor scene semantic segmentation

Semantic segmentation is a basic and long-standing research area. Depth images can enrich RGB (red-green-blue) images with their rich geometric information, so as to achieve accurate semantic segmentation. However, redundant information exists in RGB and depth images, and its handling has become an important problem. Filter group convolutions are widely used because they can eliminate redundant information and reduce computational complexity and parameter cost. Similarly, we propose a feature grouping mechanism network (FGMNet) using an attention mechanism and contextual information extraction for indoor scene semantic segmentation. First, modules of pyramid feature grouping attention and feature augmentation highlight the most useful information obtained by combining RGB and depth features. The enhanced features are then fed into a feature grouping contextual module. Results from extensive experiments on well-known indoor scene semantic segmentation datasets, NYUDv2 and SUN RGB-D, indicate that our FGMNet outperforms the most advanced existing methods in RGB-D semantic segmentation.

A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting.

Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively.

Joint superpixel and Transformer for high resolution remote sensing image classification

Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensing image (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentation use stacking on multiple scales to extract contextual information from segmented objects. However, this approach does not take into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel and Transformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input, and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixel object is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally, we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overall classification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. The effectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive and consistently better than the benchmark comparison method.

FCPFNet: Feature Complementation Network with Pyramid Fusion for Semantic Segmentation

Traditional pyramid pooling modules have shown effective improvements in semantic segmentation tasks by capturing multi-scale feature information. However, their limitations arise from the shallow structure, which fails to fully extract contextual information, and the fused multi-scale feature information lacks distinctiveness, resulting in issues with the final segmentation discriminability. To address these issues, we proposes an effective solution called FCPFNet, which is based on global contextual prior for deep feature extraction of detailed information. Specifically, we introduce a novel deep feature aggregation module to extract semantic information from the output feature map of each layer through a deep aggregation of context information module, and expands the effective perception range. Additionally, we propose an Efficient Pyramid Pooling Module (EPPM) to capture distinctive features through communicating information between different sub-features and performs multi-scale fusion, which is integrated as a branch within the network to complement the information loss resulting from downsampling operations. Furthermore, in order to ensure the richness of image detail feature information and maintain a large receptive field to obtain more contextual information, EPPM concatenates the input feature map and the output feature map of the pyramid pooling module to acquire more comprehensive global contextual information. It has been demonstrated by experiment that the method described in this article achieves competitive performance on the challenging scene segmentation datasets Pascal VOC 2012, Cityscapes and Coco-Stuff, with MIOU of 81.0%, 78.8% and 40.1%, respectively.

Two-Stage Point Cloud Registration Framework Based on Graph Neural Network and Attention

In recent years, due to the wide application of 3D vision in the fields of autonomous driving, robot navigation, and the protection of cultural heritage, 3D point cloud registration has received much attention. However, most current methods are time-consuming and are very sensitive to noises and outliers, resulting in low registration accuracy. Therefore, we propose a two-stage framework based on graph neural network and attention—TSGANet, which is effective in registering low-overlapping point cloud pairs and is robust to variable noises as well as outliers. Our method decomposes rigid transformation estimation into two stages: global estimation and fine-tuning. At the global estimation stage, multilayer perceptrons are employed to estimate a seven-dimensional vector representing rigid transformation directly from the fusion of two initial point cloud features. For the fine-tuning stage, we extract contextual information through an attentional graph neural network consisting of attention and feature-enhancing modules. A mismatch-suppression mechanism is also proposed and applied to keep our method robust to partially visible data with noises and outliers. Experiments show that our method yields a state-of-the-art performance on the ModelNet40 dataset.

Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG.

Single-channel EEG based sleep staging is of interest to researchers due to its broad application prospect in daily sleep monitoring recently. We proposed using contextual scalograms as input and developed a convolutional neural network with attention modules named Co-ScaleNet for sleep staging. The contextual scalograms were obtained by combining the same color channels of three original RGB scalograms from consecutive epochs, and a simple and efficient data augmentation was designed according to their various forms. The Co-ScaleNet consists of two main parts. Firstly, three parallel convolutional branches with attention modules correspondingly extract and fuse features from contextual scalograms at the top layers. The remaining part is a stack of lightweight blocks. We achieved an overall accuracy of 87.0% for healthy individuals, 84.7% for depressed patients. And we obtained comparable performance on the public Sleep-EDFx (82.8%), ISRUC (84.6%) and SHHS datasets (87.7%), including a high recall of N1. The contextual scalograms of R channel as input achieved the best performance, which conform to the features of interest in visual scoring. The attention modules improved the recall of N1 and N3. Overall, the contextual scalograms provided a novel scheme for both contextual information extraction and data augmentation. Our study successfully expanded its application to depression datasets, as well as patients with sleep apnea, demonstrating its wide applicability.

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP.

In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect. In order to efficiently collect ridge information, this paper proposes a segmentation method based on encoder-decoder of network with strip pooling module and ASPP module. First, in order to extract context information for multi-scale features, ASPP module are integrated in the deepest feature map. Second, the remote dependence of the ridge features is improved in both horizontal and vertical directions by using the strip pooling module. The final segmentation map is generated by fusing the boundary features and semantic features using an encoder and decoder architecture. As a result, the accuracy of the proposed method in the validation set is 98.0% and mIoU is 94.6%. The results of the experiments demonstrate that the method suggested in this paper can precisely segment the ridge information, as well as its value in obtaining data on the distribution of farmland and its potential for practical application.

Segmentation of Seabed Sediment Images Based on Convolutional Neural Network

The analysis of particle sizes in seabed sediments plays an important role in various fields. However, acquiring digital sediment images has been challenging due to the characteristics of sediment particles and the complexity of the seabed exploration environment. The goal of this study is to address the issue of particle segmentation in sediment images and propose an innovative network called SAMU-Net. Innovations are as follows. Firstly, SAMU-Net combines spatial attention modules with the traditional U-Net model, which allows for contextual information extraction and adaptive perception of feature variations. Secondly, the model adopts the idea of multi-scale fusion, enabling networks to better understand the structural and semantic information of the images by fusing feature maps at different scales. In addition, using the drop blocks and normalization, we speed up the training convergence of the model and avoid the overfitting phenomenon. The results show that the segmentation results predicted by the SAMU-Net network are highly similar to the ground truth and have a better segmentation performance compared to the existing algorithms. At the same time, it provides a new solution idea to uneven illumination and other noise interference problems in the digital imaging information of sediments.

SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.

MSDANet: A multi-scale dilation attention network for medical image segmentation

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder–decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods. Code is available at https://github.com/1999luan/MSDANet.

Striped WriNet: Automatic wrinkle segmentation based on striped attention module

Accurate segmentation of wrinkles from facial images forms the basis of wrinkle analysis and acts as an important process in skin aging evaluation. However, Wrinkles have high intraclass variability while shallow wrinkles more closely resemble the texture of the skin. Accurate segmentation of both deep wrinkles and shallow fine lines is therefore still a significant challenge. In this paper, we propose a deep learning-based automatic wrinkle segmentation model named Striped WriNet. Its central module is Striped Attention Module (SAM), which consists of Multi-Scale Striped Attention (MSA) and Global Striped Attention (GSA). The ability of the model to extract contextual information about deep folds and shallow fine lines is improved by involving the geometry of the folds to design multi-scale structures and attention mechanisms. In this paper, we conducted experiments on both the public and private datasets, where the images are finely labeled. For Accuracy (Acc), Dice Score (Dice), and Jaccard Similarity Index (JSI), the proposed method is superior to the current state-of-the-art methods. Ablation experiments and coupling tests are performed to demonstrate the effectiveness of each module and the decoupling of SAM. Finally, this paper innovatively proposes the wrinkle index as a computational method for wrinkle evaluation. The experiments were performed on 2000 wrinkle pictures, and the Pearson coefficient of wrinkle index and the expert clinical score was 0.9399, which proving the robustness of the proposed method in assessing wrinkles under different individuals. The code is available from https://github.com/mingyu6yang/striped-wrinet.

Csswin-unet: a Swin-unet network for semantic segmentation of remote sensing images by aggregating contextual information and extracting spatial information

ABSTRACTImage interpretation algorithms based on deep learning are becoming increasingly important in land cover information acquisition. We propose CSSwin-unet, a network designed for the semantic segmentation of remote sensing images. CSSwin-unet is based on Swin-unet, follows the U-shaped codec structure of U-Net, but utilizes Swin transformer blocks with superior global modelling capabilities to constitute the codec. In addition, we design a parallel branch in the encoder with a context aggregation module (CAM) to enhance contextual information extraction and alleviate the semantic ambiguity problem resulting from occlusion. To address the problem of semantic information mismatch between codecs and improve the model’s ability to extract spatial information, we constructed a space extraction module (SEM) in the skip connections, which replaces the direct copying of encoder features in Swin-unet. To reduce information loss during the downsampling process and strengthen the segmentation capacity of the network, we designed a feature shrinkage module (FSM) in the downsampling session. We conducted comprehensive ablation experiments on a dataset we produced ourselves and compared the results with other advanced methods. The test results showed significant improvement, with mIoU, mF1, and OA values improving by 2.83%, 2.47%, and 2.05%, respectively, compared to the second best performing model, Swin-unet. The above results prove the excellent performance of CSSwin-unet.

TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites

RNA N7-methylguanosine (m7G) is a crucial chemical modification of RNA molecules, whose principal duty is to maintain RNA function and protein translation. Studying and predicting RNA N7-methylguanosine sites aid in comprehending the biological function of RNA and the development of new drug therapy regimens. In the present scenario, the efficacy of techniques, specifically deep learning and machine learning, stands out in the prediction of RNA N7-methylguanosine sites, leading to improved accuracy and identification efficiency. In this study, we propose a model leveraging the transformer framework that integrates natural language processing and deep learning to predict m7G sites, called TMSC-m7G. In TMSC-m7G, a combination of multi-sense-scaled token embedding and fixed-position embedding is used to replace traditional word embedding for the extraction of contextual information from sequences. Moreover, a convolutional layer is added in the encoder to make up for the shortage of local information acquisition in transformer. The model's robustness and generalization are validated through 10-fold cross-validation and an independent dataset test. Results demonstrate outstanding performance in comparison to the most advanced models available. Among them, the Accuracy of TMSC-m7G reaches 98.70% and 92.92% on the benchmark dataset and independent dataset, respectively. To facilitate the popularization and use of the model, we have developed an intuitive online prediction tool, which is easily accessible for free at http://39.105.212.81/.