FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…In addition, the feature aggregation-spatial pyramid pooling fast module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. …”
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Breast cancer diagnosis with MFF-HistoNet: a multi-modal feature fusion network integrating CNNs and quantum tensor networks by Tariq Mahmood, Tanzila Saba, Amjad Rehman

“…MFF-HistoNet combines a CNN and a Quantum Tensor Network (QTN), which reduces model parameters through parameter compression, enabling deeper global features. The data enhancement method ensures a balanced training set and minimizes color interference. …”
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A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning by Yan Chen, Miaolin Yu, Haochong Wei, Huanxing Qi, Yiming Qin, Xiaochun Hu, Rongxing Jiang

“…The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. …”
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YOLOv8-SDC: An Improved YOLOv8n-Seg-Based Method for Grafting Feature Detection and Segmentation in Melon Rootstock Seedlings by Lixia Li, Kejian Gong, Zhihao Wang, Tingna Pan, Kai Jiang

“…Furthermore, the incorporated CA mechanism helps the model eliminate background interference for better localization and identification of seedling grafting characteristics. …”
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AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma, Jiamin Chen

“…This module uses dual-pathway encoding of high and low frequencies to enhance the focus on the details of dense small targets while reducing noise interference. Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. …”
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DSMF-Net: A One-Stage SAR Ship Detection Network Based on Deformable Strip Convolution and Multiscale Feature Refinement and Fusion by Xingyu Liu, Jun Pan, Rong Hu, Wenli Huang, Jiawei Lin, Jiarui Hu

“…To tackle these challenges, we introduce the DSMF-Net, a SAR ship detection network leveraging deformable strip convolution and multiscale feature refinement and fusion. First, to counter interference from complex backgrounds, such as nearshore ports and speckle noise, the deformable strip convolution (DSConv) is introduced and incorporated into the backbone network for SAR ship feature extraction, named SSFEBackbone. …”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…However, challenges like uneven underwater lighting, sediment interference, and complex backgrounds often hinder traditional detection methods, leading to feature loss and false detections. …”
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MASNet: mixed attention Siamese network for visual object tracking by Jianwei Zhang, Zhichen Zhang, Huanlong Zhang, Jingchao Wang, He Wang, Menya Zheng

“…However, the correlation operation directly uses the template feature to slide the window on the search area feature, and it is difficult to distinguish the target and background information when encountering similar target interference and background clutter, which can easily lead to tracking failure. …”
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FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture by Wenyu Wang, Jie Ding, Xin Shu, Wenwen Xu, Yunzhi Wu

“…The AGM module effectively suppresses background noise interference by reconstructing features and accurately capturing spatial and channel relationships, while the FESM module enhances the model’s responsiveness to disease features at different scales through channel aggregation and feature supplementation mechanisms. …”
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Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method by Lichan Zhou, Jianghui Cai, Haifeng Yang, Ali Luo, Yinbi Li, Chenhui Shi, Boyu Zhang, Jianing Tian, Yinan Yuan, Yuqing Yang

“…This paper provides 7809 carbon star candidates identified from the Large-Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) DR11 and Sloan Digital Sky Survey (SDSS) DR18, using a parallel carbon star feature recognition method. This method deploys a parallel multi-interval feature representation model and a k-nearest neighbor classification model, effectively characterizing local and global molecular bands of carbon stars and enhancing feature discrimination, especially for weak features that are highly susceptible to noise interference. …”
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Screening and Artifact Detection of RFI in Sentinel-1A Time-Series Images Combining Change Detection Techniques With Structural Similarity Index by Zhizheng Zhang, Gaofeng Shu, Yabo Huang, Lin Wu, Ning Li

“…As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. …”
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Gesture Recognition Achieved by Utilizing LoRa Signals and Deep Learning by Peihao Zhang, Baofeng Zhao

“…To counter environmental noise and static interferences, an adaptive segmentation approach based on sliding window variance analysis is introduced in the research. …”
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IDDNet: Infrared Object Detection Network Based on Multi-Scale Fusion Dehazing by Shizun Sun, Shuo Han, Junwei Xu, Jie Zhao, Ziyu Xu, Lingjie Li, Zhaoming Han, Bo Mo

“…IDDNet includes a multi-scale fusion dehazing (MSFD) module, which uses multi-scale feature fusion to eliminate haze interference while preserving key object details. …”
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ALPD-Net: a wild licorice detection network based on UAV imagery by Jing Yang, Huaibin Qin, Jianguo Dai, Guoshun Zhang, Miaomiao Xu, Yuan Qin, Jinglong Liu

“…Through adaptive channel space and positional encoding, background interference is effectively suppressed. Additionally, to enhance the model’s attention to licorice at different scales, a Lightweight Multi-Scale Module (LMSM) using multi-scale dilated convolution is introduced, significantly reducing the probability of missed detections. …”
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An improved EAE-DETR model for defect detection of server motherboard by Jian Chi, Mingke Zhang, Puhon Zhang, Guowang Niu, Zhihao Zheng

“…Subsequently, we introduced the AIFI-ASSA module, designed to mitigate background noise interference and improve sensitivity to minor defects by employing an adaptive sparse self-attention mechanism. …”
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Research Progress on Modulation Format Recognition Technology for Visible Light Communication by Shengbang Zhou, Weichang Du, Chuanqi Li, Shutian Liu, Ruiqi Li

“…This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. …”
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Advancing County-Level Potato Cultivation Area Extraction: A Novel Approach Utilizing Multi-Source Remote Sensing Imagery and the Shapley Additive Explanations–Sequential Forward S... by Qiao Li, Xueliang Fu, Honghui Li, Hao Zhou

“…We employed the harmonic analysis of NDVI time–series (HANTS) method to extract features from the time–series and evaluated the classification accuracy across five feature sets: vegetation index time–series features, band means, vegetation index means, texture features, and color space features. …”
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DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum by Kun Jiang, Kexiao Peng, Yuan Feng, Xia Guo, Zuping Tang

“…Detecting narrowband signals under broadband environments, especially under low-signal-to-noise-ratio (SNR) conditions, poses significant challenges due to the complexity of time–frequency features and noise interference. To this end, this study presents a signal detection model named deformable feature-enhanced network–You Only Look Once (DFN-YOLO), specifically designed for blind signal detection in broadband scenarios. …”
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TFC: A Series of Band Selection Methods for Hyperspectral Target Detection by Qianghui Wang, Xikui Miao, Enze Guo, Tao Zhao

“…Based on this idea, a series of BS methods called target feature constrained-based (TFC) used for the field of TD are developed and proposed in this paper. …”
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I am Once Again Asking for Your Attention: A Replication of Feature-Based Attention Modulations of Binding Effects with Picture Stimuli by Tarini Singh, Lars-Michael Schöpper, Christian Frings

“…A repetition of any of these features results in a retrieval of the entire episodic trace, and can thus facilitate or interfere with future actions. …”
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