Multiscale Task-Decoupled Oriented SAR Ship Detection Network Based on Size-Aware Balanced Strategy by Shun He, Ruirui Yuan, Zhiwei Yang, Jiaxue Liu

“…First, the multiscale target features are extracted using the multikernel heterogeneous perception module (MKHP). Meanwhile, the triple-attention module is introduced to establish the remote channel dependence to alleviate the issue of small target feature annihilation, which can effectively enhance the feature characterization ability of the model. …”
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Self-supervised deep learning for detection of forest disturbance types in a subtropical ecosystem using transformer and Sentinel-1 and Sentinel-2 time series data by Ming Zhang, Guiying Li, Dengsheng Lu, Cong Xu, Haotian Zhao, Dengqiu Li

“…In this study, a novel positional encoding module was designed to handle the irregular Sentinel-2 time series. …”
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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

“…Therefore, a novel nucleus segmentation method based on the U-Net architecture is proposed to overcome this issue. Firstly, we introduce a Weighted Feature Enhancement Unit (WFEU) in the encoder decoder fusion stage of U-Net. …”
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A Joint Knowledge Extraction Model for Tobacco Pest and Disease Prevention Based on BERT+BA+CASREL by Kehan Liu, Feng Zhang, Qiulan Wu, Xiang Sun, Huarui Wu, Ziruo Sun

“…Additionally, the GHM loss function is adopted to replace the traditional cross-entropy loss function, alleviating the data imbalance issue. Experimental results demonstrate that the proposed model achieves a precision of 93.32%, a recall of 92.51%, and an F1-score of 92.91%, validating its effectiveness in addressing long-text overlapping triplet extraction and other related issues. …”
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MFFNet: a building change detection method based on fusion of spectral and geometric information by Zhihao Guo, Jianping Pan, Peng Xie, Ling Zhu, Chen Qi, Xunxun Wang, Yihan Yang, Yan Wang, Huijuan Zhang, Zhaohui Ren

“…However, when using remote sensing images, shadows, vegetation and objects with similar spectral and morphological characteristics as buildings can cause false detections, omissions and incomplete patch edges. To address this issue, we develop the multiscale feature fusion network for dual-modal data (MFFNet), which has two main aspects: (1) The multi-dual-modal feature fusion module detects changes in features with similar spectral and morphological characteristics as buildings. …”
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Research on multi-view collaborative detection system for UAV swarms based on Pix2Pix framework and BAM attention mechanism by Yan Ding, Qingxin Cao, Bozhi Zhang, Peilin Li, Zhongjiao Shi

“…A visual attention module (BAM) is employed to manage appearance differences under varying angles, while a feature mapping module (DFM) prevents fine-grained feature loss. …”
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Hybrid attentive prototypical network for few-shot action recognition by Zanxi Ruan, Yingmei Wei, Yanming Guo, Yuxiang Xie

“…We further propose a prototypical attentive matching module (PAM) built on the concept of metric learning to resolve the overfitting issue common in few-shot tasks. …”
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Ship Detection Transformer in SAR Images Based on Key Scattering Points Feature Aggregation and Context Feature Refinement by Yifei Yin, Zhu Yang, Hao Shi, Fanyu Meng, Wei Li

“…Furthermore, to address the issue of excessive false alarms under complex background interference, a context feature refinement module is designed to augment the semantic representation and context information of feature maps. …”
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FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images by Bochao Chen, Yapeng Wang, Xu Yang, Xiaochen Yuan, Sio Kei Im

“…This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. …”
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CenterNet-Elite: A Small Object Detection Model for Driving Scenario by Lingling Wang, Xiang Li, Xiaoyan Chen, Bin Zhou

“…We introduce a multi-scale pooling module, SPPCSPC, to address the challenge of significant variations in object scale. …”
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A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling by Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang

“…By avoiding the problem of overlap in dense areas, the optimized label maps achieve a good balance between counting accuracy and localization, with MAE and MSE reaching 64.1 and 103.9 in SHHA, and 10.9 and 17.4 in SHHB, respectively.Secondly, to address the scale insensitivity of the encoder and the potential loss of critical features during the encoding process, we propose the Adaptive Feature Fusion Module and the Multi-Scale Global Attention Upsampling Module, constructing the CALNET network. …”
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Denoising and Recognition Method for Weak Acoustic Abnormal Signals in Hot-Wall Hydrogenation Reactors Using DnCNN-CNN by Xueqin Wang, Shilin Xu, Yun Tu, Ying Zhang, Mingguo Peng

“…Short-Time Fourier Transform (STFT) is employed to convert the AE time-domain signals into time-frequency domain joint representations, constructing a compressed spectrogram as the network input. The DnCNN module removes noise from the mixed spectrogram, while the CNN module performs signal classification. …”
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Language-Guided Semantic Clustering for Remote Sensing Change Detection by Shenglong Hu, Yiting Bian, Bin Chen, Huihui Song, Kaihua Zhang

“…Afterward, a CLIP adapter module (CAM) is designed to fine-tune the semantic embeddings to align with the change region embeddings from the input bi-temporal images. …”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…Additionally, we replace the Spatial Pyramid Pooling Fast (SPPF) module in YOLOv8 with the Feature Pyramid Shared Convolution (FPSC) module. …”
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A simple monocular depth estimation network for balancing complexity and accuracy by Xuanxuan Liu, Shuai Tang, Mengdie Feng, Xueqi Guo, Yanru Zhang, Yan Wang

“…For the extraction of finer local features, we propose a Local Multi-dimensional Convolutional Attention (LMC) module. Meanwhile, we propose a Wavelet Attention Transformer (WAT) module to achieve pixel-level precise classification of images. …”
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Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images by Kaiyuan Long, Shibo Li, Jiangping Long, Hui Lin, Yang Yin

“…Compared to the YOLOv8 network, the proposed YOLO-PH network incorporates the C2f_DyGhostConv module as a replacement for the original C2f module in both the backbone network and neck network. …”
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RoFDiff: Robust Hyperpansharpening via a High-Low Frequency Conditional Diffusion Model by Jiangtao Huang, Ting Luo, Zhouyan He, Haiyong Xu, Leyuan Fang

“…To enhance the reconstructed HFFs, the spectral fusion module leverages interspectral correlations to improve spectral fidelity, while the spectral-spatial reconstruction module utilizes multiscale spatial relationships to refine texture details. …”
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Infrared Image Classification and Detection Algorithm for Power Equipment Based on Improved YOLOv10 by Xiu Ji, Zheyu Yue, Hongliu Yang, Zehong Zhang

“…Secondly, a Slim Neck design structure is used in the neck network and combined with a dual convolution module (DualConv) to achieve a lightweight model. …”
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ELNet: An Efficient and Lightweight Network for Small Object Detection in UAV Imagery by Hui Li, Jianbo Ma, Jianlin Zhang

“…First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. …”
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Research on highway road condition intelligent assessment and optimization system based on deep learning and internet of things by Tingquan He, Changhai Wang, Riyan Lan, Haiyu Luo

“…By optimizing the ResNet-50 down sampling module, introducing the channel attention mechanism, and improving the NMS strategy, the detection accuracy is significantly improved to 89.63 % mAP while maintaining efficient processing speed. …”
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