A Variational Multi-Scale Error Compensation Network for Single-Pixel Imaging by Jian Lin, Qiurong Yan, Quan Zou, Shida Sun, Zhen Wei, Hua Du

“…We employ multiple latent variables to generate error features at different scales in the intermediate layers of the error compensation network, compensating the reconstructed image. …”
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Partial-Net: A Method for Data Gaps Reconstruction on Mars Images by Depei Gu, Dingruibo Miao, Jianguo Yan, Zhigang Tu, Jean-Pierre Barriot

“…The mask self-updating mechanism is applied simultaneously following the partial convolution of each layer, effectively tracking the shape of the mask and reconstructing missing areas during forward propagation. …”
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Simulation Analysis of Soft Focusing of Azimuth Laterolog Resistivity Imaging LWD Tool by TANG Zhanghong, YANG Zhiliang, CHEN Gang, ZHANG Guoyan, ZHAI Xingyu, SONG Sen

“…The current flowing out of the drill collar will produce axial components, which cannot flow out strictly perpendicular to the surface of the drill collar, resulting in inaccurate measurement results. To address this issue, five soft focusing working modes are designed based on the electrode structure of the azimuth laterolog resistivity imaging logging tool (RIT), and expressions for calculating the azimuth lateral electrode current and apparent resistivity under different working modes are provided. …”
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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…Second, we design a dual-scale constrained graph U-Nets encoder, and use an attention feature fusion module dynamically weights these multiscale representations using channel-wise attention coefficients, effectively resolving feature redundancy issues. Finally, we introduce the graph attention network with contrastive normalization layer module to replace traditional GCNs, enabling dynamic graph structure updating during propagation alleviating over-smoothing through differential feature enhancement. …”
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Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation by Feng-Ping An, Jun-e Liu

“…To address these issues, this paper first adapts a neural network to medical image features by adding cross-layer connections to a traditional convolutional neural network. …”
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Research on Method for Intelligent Recognition of Deep-Sea Biological Images Based on PSVG-YOLOv8n by Dali Chen, Xianpeng Shi, Jichao Yang, Xiang Gao, Yugang Ren

“…In the neck network, a Slim-Neck module (GSconv + VoVGSCSP) is incorporated to reduce the parameter count and model size while simultaneously augmenting the detection performance. Moreover, the introduction of a squeeze–excitation residual module (C2f_SENetV2), which leverages a multi-branch fully connected layer, further bolsters the network’s global representational capacity. …”
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A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE by Van-Hung Le, Thi-Ha-Phuong Nguyen

“…Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D image data includes two main tasks: One is to build an environment map, and the other is to simultaneously track the position and movement of visual odometry estimation (VOE). …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. …”
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YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images by Yin Pan, Zhenpeng Zhang, Xueyang Zhang, Zhi Zeng, Yibin Tian

“…First, ResConv is designed to ensure the transmission of discriminative features of small objects during feature propagation, leveraging the ability of residual connections to transmit information from one layer to the next. Second, to tackle the issue of weak focusing capabilities on small targets, a Token Attention module is introduced before the third small object detection head. …”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Furthermore, we introduce a dual-branch ensemble network to address the issue of image variety. This network uses two branches to extract image features at different resolutions for fusion, thereby expanding the network receptive field. …”
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BreastCNet: Breast Cancer Detection, Classification, and Localization Convolutional Neural Network With Advanced Optimization Techniques by Hassan Mahichi, Vahid Ghods, Mohammad Karim Sohrabi, Arash Sabbaghi

“…The multi-task learning framework simultaneously performed breast ultrasound image classification (benign/malignant) and lesion localization via bounding box regression. …”
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A low illumination target detection method based on a dynamic gradient gain allocation strategy by Zhiqiang Li, Jian Xiang, Jiawen Duan

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Potential of diagnostic electron microscopy of calcification, pathological neovascularization and elastolysis in combination with phenotyping of cell populations in large arteries by R. A. Mukhamadiyarov, K. Yu. Klyshnikov, V. A. Koshelev, A. G. Kutikhin

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GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

“…Simultaneously, session images were processed by the spatio-temporal feature extraction (SFE) module, of which the spatial features of packets were extracted by a convolutional neural network with residual connections, and temporal features of packets were extracted by a bi-directional long short-term memory network. …”
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Improved Asphalt Pavement Crack Detection Model Based on Shuffle Attention and Feature Fusion by Tursun Mamat, Abdukeram Dolkun, Runchang He, Yonghui Zhang, Zulipapar Nigat, Hanchen Du

“…Initially, we establish the required dataset and classify images proportionally based on their states. Subsequently, we conduct comparative testing against the results of the original model, analyzing issues such as the oversight of shallow and small cracks, truncation in the recognition of single-instance long cracks, and imprecise detection. …”
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Space 4.0 – a common, democratic European space, part 5 by Ryszard S. Romaniuk, Piotr Orleański

“…Our intention was to present the image of the European space sector in an optimistic way, but simultaneously we did not omit some critical reflections. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…However, there is a short-fall in detailed research on optimizing models for weed identification with images from mobile embedded systems. Also, existing methods generally use large, slow multi-layer convolutional networks (CNNs), which are impractical for use on mobile embedded devices. …”
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Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI by T. M. Nagao, K. Suzuki, M. Kuji

“…These include the bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds.</p>…”
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A Framework for Symmetric-Quality S3D Video Streaming Services by Juhyeon Lee, Seungjun Lee, Sunghoon Kim, Dongwook Kang

“…This paper proposes an efficient encoding framework based on Scalable High Efficiency Video Coding (SHVC) technology, which supports both low- and high-resolution 2D videos as well as stereo 3D (S3D) video simultaneously. Previous studies have introduced Cross-View SHVC, which encodes two videos with different viewpoints and resolutions using a Cross-View SHVC encoder, where the low-resolution video is encoded as the base layer and the other video as the enhancement layer. …”
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Real-Time Detection of Varieties and Defects in Moving Corn Seeds Based on YOLO-SBWL by Yuhang Che, Hongyi Bai, Laijun Sun, Yanru Fang, Xinbo Guo, Shanbing Yin

“…To address these issues, this study proposed a real-time detection model, YOLO-SBWL, that simultaneously identifies corn seed varieties and surface defects by using images taken at different conveyor speeds. …”
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