A crop model based on dual attention mechanism for large area adaptive yield prediction by Wei Xiang, Long Long, Zichen Liu, Feng Dai, Yucheng Zhang, Hu Li, Lin Cheng

“…Although existing models have improved accuracy by increasing model complexity and coupling different deep learning models, their generalization performance is poor due to significant spatial differences in crop growth environments, making it difficult to explore common features of crop environments in different regions.To address this issue, this paper comprehensively considers crop growth cycles and environmental factors such as soil and weather, presenting a large-scale crop yield prediction model based on an attention mechanism.The model consists of two modules: time attention module and feature attention module. …”
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Hash-Guided Adaptive Matching and Progressive Multi-Scale Aggregation for Reference-Based Image Super-Resolution by Lin Wang, Jiaqi Zhang, Huan Kang, Haonan Su, Minghua Zhao

“…Firstly, to address the issue of feature matching, this chapter proposes a hash adaptive matching module. …”
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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…The DSDM-LFIM backbone network, which combines Deep-Shallow Downsampling Modules (DSD Modules) and Lightweight Feature Integration Modules (LFI Modules), avoids excessive use of group convolutions and element-wise operations. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…Firstly, the FPC-F (Fast Parallel Cross-Fusion) module, which incorporates PConv, and the S-EMCP (Space-efficient Merging Convolution Pooling) module are designed in the backbone network to replace C2F and SPPF at various scale branches. …”
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Multi dynamic temporal representation graph convolutional network for traffic flow prediction by Zuojun Wu, Xiaojun Liu, Xiaoling Zhang

“…Moreover, a multiaspect fusion module is presented, which combines auxiliary hidden states learned from traffic volume with primary hidden states derived from traffic speed. …”
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Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration by Haolin Chang, Zhihua Xie, Fan Yang

“…Specifically, to obtain cross-modal complementary information, this scheme sequentially constructs a Multi-Scale Feature Extraction Module (MFEM) and a Multi-scale hierarchical Semantic-Guided Fusion Module (MSGFM). …”
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Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation by SUN Xiujuan, SUN Fuzhen, LI Pengcheng, WANG Aofei, WANG Shaoqing

“…In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks, a model called GATSR, which is based on graph attention networks for sequential recommendation, is proposed. …”
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A lip reading method based on adaptive pooling attention Transformer by YAO Yun, HU Zhenxiao, DENG Tao, WANG Xiao

“…This module effectively suppressed irrelevant information and enhances the representation of key features. …”
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Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism by JIANG Di, LAI Huicheng, WANG Liejun

“…Secondly, to address the issue of modal asynchrony in multimodal data, a modal consistency alignment module was proposed, which aligned modal semantics along the temporal frame sequence to ensure both temporal and semantic consistency in multimodal data. …”
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The lip reading method based on Adaptive Pooling Attention Transformer by YAO Yun, HU Zhenxiao, DENG Tao, WANG Xiao

“…This module helps suppress irrelevant information and enhances the representation of key features. …”
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A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…Afterwards, the Swin-Transformer is integrated with the C3 module in the Neck to build the C3STR module, which aims to address the issue of cluttered background in defective images and the confusion caused by simple defect types. …”
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Generative Adversarial Network-Based Distortion Reduction Adapted to Peak Signal-to-Noise Ratio Parameters in VVC by Weihao Deng, Zhenglong Yang

“…The lightweight attention module is integrated into the residual block of the generator module structure, thereby facilitating the extraction of image details and motion compensation. …”
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A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images by Lei Zhang, Qing Zhang, Yu Wu, Yanfeng Zhang, Shan Xiang, Donghai Xie, Zeyu Wang

“…Finally, to address the issue of important information from the other two modules being lost due to continuous upsampling during the decoding phase, we proposed an auxiliary branch module to assist the main branch in decision-making, ensuring that the final output retained the key information from all stages. …”
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Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery by Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ul-Haq, David Blake, Naeem Janjua

“…The technique comprises three key components: a spectral-spatial convolutional module (SSCM), a spatial attention module (SAM), and a transformer module (TM). …”
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DADNet: text detection of arbitrary shapes from drone perspective based on boundary adaptation by Jun Liu, Jianxun Zhang, Ting Tang, Shengyuan Wu

“…Using ResNet50 as the backbone network, we introduce the proposed Hybrid Text Attention Mechanism into the backbone network to enhance the perception of text regions in the feature extraction module. Additionally, we propose a Spatial Feature Fusion Module to adaptively fuse text features of different scales, thereby enhancing the model’s adaptability. …”
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A Lightweight Pavement Defect Detection Algorithm Integrating Perception Enhancement and Feature Optimization by Xiang Zhang, Xiaopeng Wang, Zhuorang Yang

“…The algorithm first designs the Receptive-Field Convolutional Block Attention Module Convolution (RFCBAMConv) and the Receptive-Field Convolutional Block Attention Module C2f-RFCBAM, based on which we construct an efficient Perception Enhanced Feature Extraction Network (PEFNet) that enhances multi-scale feature extraction capability by dynamically adjusting the receptive field. …”
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GDSN-CD: Graph-Guided Diffusion Synergistic Network for Remote Sensing Change Detection by Yunfei Zhu, Jintao Song, Jinjiang Li

“…Furthermore, we propose a hierarchical dynamic fusion module that adaptively fuses multilevel features to coordinate the complementary relationship between texture and semantics; and design a dual-feature dynamic selection module that optimizes the fusion of differential and additive features through adaptive weighting, accurately enhancing change signals while suppressing background interference. …”
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Multiscale Interaction Purification-Based Global Context Network for Industrial Process Fault Diagnosis by Yukun Huang, Jianchang Liu, Peng Xu, Lin Jiang, Xiaoyu Sun, Haotian Tang

“…First, we propose a multiscale feature interaction refinement (MFIR) module. The module aims to extract multiscale features enriched with combined information through feature interaction while refining feature representations by employing the efficient channel attention mechanism. …”
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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…[Methods] In this paper, a two-stream network called the Two-stream Feature Enhancement Network (TFEN) was proposed to address these issues. To solve the problem of feature damage caused by temporal shift, a Spatial Enhancement-Temporal Shift Module (SE-TSM) and a Channel Enhancement-Temporal Shift Module (CE-TSM) were proposed to enhance features after each time shift, for improving damaged features. …”
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Error Mitigation Teacher for Semi-Supervised Remote Sensing Object Detection by Junhong Lu, Hao Chen, Pengfei Gao, Yu Wang

“…EMT consists of three lightweight modules. First, the Adaptive Pseudo-Label Filtering (APLF) module removes noisy pseudo boxes via a second-stage RCNN and adjusts class-specific thresholds through dynamic confidence filtering. …”
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