Exploring latent weight factors and global information for food-oriented cross-modal retrieval by Wenyu Zhao, Dong Zhou, Buqing Cao, Wei Liang, Nitin Sukhija

“…Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Our approach leverages <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><mn>1</mn></mrow></semantics></math></inline-formula> convolutional kernels within a convolutional neural network (CNN) solver to approximate derivatives, enabling the discovery of interpretable equations that generalize across temporal–spatial domains. …”
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Spectrum sensing based on adversarial transfer learning by Jiawu Miao, Yuebo Li, Xiaojun Jing, Fangpei Zhang, Junsheng Mu

“…After that, the pre‐trained CNN model is distributed to local nodes with different SNRs and the pre‐trained CNN model is fine‐tuned. …”
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Linguistic-visual based multimodal Yi character recognition by Haipeng Sun, Xueyan Ding, Zimeng Li, Jian Sun, Hua Yu, Jianxin Zhang

“…The visual transformer, integrated with deformable convolution, effectively captures key features during the visual modeling phase. …”
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LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation by Ningkang Hu, Bing Li, Hong Peng, Zhicai Liu, Jun Wang

“…LMFUNet uses an Efficient Multi-scale Feature Extraction block (EMFE) in deep stages, which uses grouping of features by convolution with different dilation rates to reduce model complexity and effectively capture multi-scale features. …”
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A Lightweight Fault Diagnosis Method Based on Knowledge Distillation Under Time-Varying Rotational Speeds by Xiwang Yang, Yarong Wang, Lele Gao, Jia Luo, Licheng Jing, Jinying Huang, Guangpu Liu, Chenfeng Yang

“…However, collecting a large amount of fault information of different working conditions in the engineering environment is difficult, and the lack of fault data is a problem that has been difficult to solve. …”
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Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications by Jerome J. Malone, Byeong Kil Lee

“…Optimum memory for equalization of QPSK, 8-PSK, and 16-QAM was found to be four linear and four or five cubic memory units. Difference in performance between fully-coupled and partially-decoupled adaptive equalizers was found to be small, rarely exceeding ~1.5 dB mean-square error. …”
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Evaluation of a Deep Learning Model for Automatic Detection of Schizophrenia Using EEG Signals by Swetha Padmavathi Polisetty, Radhamani Ellapparaj, Karthikeyan M P

“…After data preprocessing to reduce noise and artifacts from EEGs, an 11-layer deep learning model consisting of convolution and LSTM layers with LeakyReLU activation function and different kernel sizes was implemented to automatically extract and classify features. …”
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Enhancing anemia detection through multimodal data fusion: a non-invasive approach using EHRs and conjunctiva images by Muhammad Ramzan, Muhammad Usman Saeed, Ghulam Ali

“…The features from the conjunctiva images are extracted using RCBAM (Reverse Convolution Block Attention Mechanism). After that, GRAD-Cam algorithm is applied to calculate the pixel percentages of all the features. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…The method incorporates a multi-scale temporal attention module and a multi-scale temporal convolution module to extract multi-scale information. …”
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Dynamic characteristic analysis and load design of large floating structures based on experimental design by Guofeng Zhao, Shifan Zhu

“…In order to make qualitative and quantitative analysis of the regularity, the hydrodynamic response of large-scale ocean engineering structures under different wave loads is experimentally designed and studied based on the experimental design method and the moving least squares–Kriging hybrid fitting method. …”
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UAV-to-Ground Target Detection Based on YOLO-DSBE by Meng Pengshuai, Wang Feng, Zhai Weiguang, Ma Xingyu, Zhao Wei

“…To address the issues of complex background, small target scale, mutual occlusion and high missed detection rate in UAV captured images, this paper proposes a ground target detection algorithm based on YOLO-DSBE.The DC-ELAN and DC-MP modules based on deformable convolution are proposed to adapt to input features of different shapes and sizes, and to improve the network’s ability to parse features in complex backgrounds; A high-resolution multi-scale detection layer is designed to boost the algorithm’s capability in extracting small target features, thereby improving the detection accuracy of minute targets. …”
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Research on semantic segmentation of parents in hybrid rice breeding based on improved DeepLabV3+ network model by WEN Jia, LIANG Xifeng, WANG Yongwei

“…Compared with other mainstream network models and advanced network models, it is found that the accuracy of different parameters of improved DeepLabV3+ network model is improved. …”
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Fish feeding behavior recognition model based on the fusion of visual and water quality features by Zheng ZHANG, Bosheng ZOU

“…To better capture the global features of different aggregation levels and the detailed features of feeding behavior, a context-aware local attention mechanism (Cloatt) was introduced in each convolution stage of ConvNeXtV2-T. …”
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Segmented Frequency-Domain Correlation Prediction Model for Long-Term Time Series Forecasting Using Transformer by Haozhuo Tong, Lingyun Kong, Jie Liu, Shiyan Gao, Yilu Xu, Yuezhe Chen

“…Furthermore, we introduce an isometry convolution method to enhance the prediction accuracy of the model. …”
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An improved multi-object instance segmentation based on deep learning by Nawaf Alshdaifat, Mohd Azam Osman, Abdullah Zawawi Talib

“…The findings also revealed that in terms of average precision over IoU (AP) threshold measurements using different thresholds, the proposed approach obtained improved results compared to other well-known segmentation approaches. …”
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A lightweight and efficient gesture recognizer for traffic police commands using spatiotemporal feature fusion by Jun Xiao, Honghan Li, Ji Zhao

“…Initially, keypoints related to traffic police gestures are extracted using the Efficient Progressive Feature Fusion Network (EPFFNet), followed by feature modeling and fusion to enable the recognition network to better learn the temporal characteristics of gestures. Additionally, a convolution network branch and a hybrid attention branch are incorporated to further extract skeleton information from the traffic police gesture data, assign different temporal weights to key frames, and enhance the focus on important channels. …”
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Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory by Lin Gan, Mu Zhang, Jiajia Jiang, Fajie Duan

“…Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. …”
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Method of Non-Destructive Control of Single-Phase and Three-Phase Transformers's Condition on the Basis of Frequency Characteristics by I. L. Hramyka, V. N. Galushko

“…Nowadays, there are many different methods of transformer diagnostics. The analysis of used methods and diagnostic systems indicates that a certain complexity of further development of existing methods and diagnostic systems has been achieved. …”
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ACGRHA-Net: Accelerated multi-contrast MR imaging with adjacency complementary graph assisted residual hybrid attention network by Haotian Zhang, Qiaoyu Ma, Yiran Qiu, Zongying Lai

“…Extensive experiments on the different datasets, using various sampling patterns and accelerated factors demonstrate that the proposed method outperforms the current state-of-the-art reconstruction methods.…”
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