A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data by Juan Tian, Shun Zhang, Gang Xie, Hui Shi

“…It enhances diagnostic performance in the target domain by transferring knowledge across diverse domains. DS-MDAN uses convolution kernels of different scales to extract multi-scale feature information and achieves feature fusion through upsampling and operations like addition and concatenation. …”
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AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model’s performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. …”
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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. …”
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FruitQuery: A lightweight query-based instance segmentation model for in-field fruit ripeness determination by Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo

“…FruitQuery runs in an end-to-end way and incorporates the convolution and Transformer to capture fine-grained features related to different fruits at different ripeness stages.Extensive experiments on the combined fruit dataset demonstrate that our FruitQuery achieves the highest average precision of 67.02 with only 14.08M parameters, outperforming 13 state-of-the-art models with 33 variants. …”
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Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…A cross-level contrastive learning paradigm is also designed to combine item representations from different levels in order to enhance the semantic information between items at different levels. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model by QIU Xinhua, WANG Wenkun, JI Yuwen, LI Jia

“…Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accuracy in the face of different illumination, complex background and different forms of image. …”
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Multi-camera video collaborative analysis method based on edge computing by Zhibo QI, Lei DU, Ru HUO, Fan YANG, Tao HUANG

“…In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.…”
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Adaptive Conditional Reasoning for Remote Sensing Visual Question Answering by Yiqun Gao, Zongwen Bai, Meili Zhou, Bolin Jia, Peiqi Gao, Rui Zhu

“…In order to enhance the multimodal fusion process of different types of questions, the ACR model further integrates visual and textual features by leveraging type-guided cross-attention. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…In order to improve the feature extraction ability and fault diagnosis accuracy of fault diagnosis models, based on the lightweight convolutional neural network MobileNetV2, the convolutional block attention mechanism CBAM is integrated into the deep feature mapping of MobileNetV2, and an attention mechanism convolutional classification network CBAM-MobileNetV2 is constructed. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The Dilated Encoder network is introduced to cover different-scale targets by differentiating receptive fields, and the feature weight allocation is optimized by combining it with a Convolutional Block Attention Module (CBAM). …”
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Detection of Pathogenic Microorganisms by Fusion of Recursive Feature Pyramid by HUANGZhitian, XIEYining, ZHAOJing, HEYongjun

“…Aiming at the problems of less research on the detection of pathogenic microorganisms , large differences in target size and complex background , a multi-scale detection method of pathogenic microorganisms fused with recursive feature pyramid was proposed. …”
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Deep vision-based real-time hand gesture recognition: a review by Cui Cui, Mohd Shahrizal Sunar, Goh Eg Su

“…The choice of evaluation metrics and dataset is critical since different tasks require different evaluation parameters, and the model learns more patterns and features from diverse data. …”
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A Survey on Immersive Cyber Situational Awareness Systems by Hussain Ahmad, Faheem Ullah, Rehan Jafri

“…However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. …”
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Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. …”
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TMFN: a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning for multimodal sentiment analysis by Junsong Fu, Youjia Fu, Huixia Xue, Zihao Xu

“…Firstly, we propose an innovative pyramid-structured multi-scale feature extraction method, which captures the multi-scale features of modal data through convolution kernels of different sizes and strengthens key features through channel attention mechanism. …”
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Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI by Asma Naseer, Tahreem Yasir, Arifah Azhar, Tanzeela Shakeel, Kashif Zafar

“…The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. …”
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Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling by Shuqin Li, Saisai Li, Hengyang Cao, Kun Meng, Meng Ding

“…Role modeling learns different roles and behaviors by using a convolutional neural network. …”
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Time series image coding classification theory based on Lagrange multiplier method by Wentao Jiang, Ming Zhao, Hongbo Li

“…Recent advancements in convolutional neural networks (CNNs) for image recognition and classification have inspired innovative approaches in this field. …”
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Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (<i>Brassica rapa</i> subsp. <i>Pekinensis</i>) Plants by Xiandan Du, Zhongfa Zhou, Denghong Huang

“…The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. …”
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