SEMA-YOLO: Lightweight Small Object Detection in Remote Sensing Image via Shallow-Layer Enhancement and Multi-Scale Adaptation by Zhenchuan Wu, Hang Zhen, Xiaoxinxi Zhang, Xuechen Bai, Xinghua Li

“…Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

“…Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. …”
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MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba by Jianqiang Zhang, Jing Hou, Qiusheng He, Zhengwei Yuan, Hao Xue

“…We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. …”
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A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering by Zhan Xing, Jianhui Lin, Yan Huang, Cai Yi

“…However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. …”
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Enhanced efficiency and security in cross-chain transmission of blockchain internet of ports through multi-feature-based joint learning by Zeqiang Xie, Xiaowei Zhang, Xinbing Liu

“…The proposed method integrates multi-feature joint learning with adaptive multi-channel joint bus control, enabling dynamic resource allocation and interference suppression for enhanced transmission efficiency. …”
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2HR-Net VSLAM: Robust visual SLAM based on dual high-reliability feature matching in dynamic environments. by Wang Yang, Huang Chao, Zhang Yi, Tan Shuyi

“…Visual Simultaneous Localization and Mapping (VSLAM) is the key technology for autonomous navigation of mobile robots. However, feature-based VSLAM systems still face two major challenges in dynamic complex environments: insufficient feature reliability and significant dynamic interference, urgently requiring improved matching robustness. …”
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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

“…The spatial-frequency feature fusion combines features in spatial and frequency domains, which enhances feature representation capability and learns local and long-range feature correlations. …”
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LMAD-YOLO: A vehicle image detection algorithm for drone aerial photography based on multi-scale feature fusion. by Xue Xing, Fahui Luo, Le Wan, Kang Lu, Yuqi Peng, Xiujuan Tian

“…The LMAD-YOLO model is proposed, and the MultiEdgeEnhancer module is designed to enhance the edge information and enhance the feature capture through a series of operations. Large Separable Kernel Attention and SPPF are combined to form MSPF module, which can realize multi-scale perception aggregation and improve the ability of distinguishing small targets from interference. …”
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YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 by Junchao Yuan, Lina Wang, Tingting Wang, Ali Kashif Bashir, Maryam M. Al Dabel, Jiaxing Wang, Hailin Feng, Kai Fang, Wei Wang

“…Furthermore, by incorporating the dynamic upsampling operator, our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. …”
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A Dual-Stream Dental Panoramic X-Ray Image Segmentation Method Based on Transformer Heterogeneous Feature Complementation by Tian Ma, Jiahui Li, Zhenrui Dang, Yawen Li, Yuancheng Li

“…To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. …”
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Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. …”
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An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition by Haiying Wang, Wei Lu, Yingying Wu, Qunying Zhang, Xiaojun Liu, Guangyou Fang

“…Synthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and limited engineering applicability under low signal-to-noise ratio (SNR) conditions, an enhanced algorithm named dual-input feature fusion ShuffleNet (DIFF-ShuffleNet) based on intercepted SAR signal data is proposed. …”
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BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion by Bin Han, Xin Huang, Feng Xue

“…However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. …”
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An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…Abstract In small object detection scenarios such as UAV aerial imagery and remote sensing, the difficulties in feature extraction are primarily due to challenges such as small object size, multi-scale variations, and background interference. …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…For facial keypoints, we transform data into images and employ MobileVitv2 for transfer learning to capture facial and head movement features. Ultimately, a feature fusion module is designed to fuse the features from both branches, yielding the final action category prediction. …”
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TCI-Net: Structural Feature Enhancement and Multi-Level Constrained Network for Reliable Thin Crack Identification on Concrete Surfaces by Shiming Li, Fengtao Yan, Zhijie Li, Qingfeng Hu, Shiqian Xu, Shilian Liu

“…This method builds on a pre-trained multi-scale semantic feature encoding network, integrating various crack edge structure information extraction operators, and a lightweight crack spatial detail feature extraction module is constructed. …”
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Extraction of Garlic in the North China Plain Using Multi-Feature Combinations from Active and Passive Time Series Data by Chuang Peng, Binglong Gao, Wei Wang, Wenji Zhu, Yongqi Chen, Chao Dong

“…In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. …”
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Enhanced Extraction of Activation Time and Contractility From Myocardial Strain Data Using Parameter Space Features and Computational Simulations by Borut Kirn

“…This study investigates whether leveraging features in the parameter space can enhance parameter extraction. …”
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Full-duplex device-to-device communications:key technologies and prospects by Jiaxin DING, Daquan FENG, Gongbin QIAN, Nan ZHANG

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Cognitive Dominants, Language-Specific Communication and Translation Problems by N. K. Riabtseva

“…Special attention in the paper is paid to the translation into the foreign, English, language, its contrastive culture-specific and communicative features as compared to those in the Russian language: to their cognitive dominants in communication and their cross-linguistic asymmetry and in-congruency which generate quite «natural» cross-linguistic interference in Russian-English translation. …”
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