RGB-T Object Detection With Failure Scenarios by Qingwang Wang, Yuxuan Sun, Yongke Chi, Tao Shen

“…To further address the issue of redundant information in existing RGB-T object detection models, a redundant information suppression module is introduced, minimizing cross-modal redundant information based on mutual information and contrastive loss. …”
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ADS-YOLO: A Multi-Scale Feature Extraction Remote Sensing Image Object Detection Algorithm Based on Dilated Residuals by Jianying Li, Yajun Chen, Meiqi Niu, Wenhao Cai, Xiaoyang Qiu

“…Lastly, to address the issue of dense targets, we design the Soft-NMS-ShapeIoU module to improve the consistency of bounding boxes and target shapes, while also suppressing adjacent boxes. …”
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Sonar-based object detection for autonomous underwater vehicles in marine environments by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Yucheng Zhang

“…To tackle the issue of object scale variation, we designed a multi-scale feature refinement module (MFRM) to improve both classification accuracy and positional precision by refining the feature representations of objects at different scales. …”
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MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…The primary structure of this network adopts the U-Net architecture. To address the issue of lacking texture and structural information in the target images, we employ an integrated differential convolution (IDConv) module to extract richer image features during both the encoding and decoding stages. …”
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Enhancing students performance through dynamic personalized learning path using ant colony and item response theory (ACOIRT) by Imamah, Umi Laili Yuhana, Arif Djunaidy, Mauridhi Hery Purnomo

“…The parameters used to create the PLP include the initial module, targeted module, knowledge level, pretest score, and difficulty level. …”
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TSFANet: Trans-Mamba Hybrid Network with Semantic Feature Alignment for Remote Sensing Salient Object Detection by Jiayuan Li, Zhen Wang, Nan Xu, Chuanlei Zhang

“…However, the deeper underlying issue lies in how to effectively align and integrate local detail features with global semantic information. …”
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PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

“…This approach treats alignment quality as a learnable, spatially-varying confidence and materializes this concept through three synergistic modules. The Progressive Deformable Feature Alignment (PDFA) module achieves robust feature extraction through a two-stage deformable convolution and exposure-aware modulation. …”
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Image denoising algorithm based on multi-channel GAN by Hongyan WANG, Xiao YANG, Yanchao JIANG, Zumin WANG

“…Aiming at the issue that the noise generated during image acquisition and transmission would degrade the ability of subsequent image processing, a generative adversarial network (GAN) based multi-channel image denoising algorithm was developed.The noisy color image could be separated into red-green-blue (RGB) three channels via the proposed approach, and then the denoising could be implemented in each channel on the basis of an end-to-end trainable GAN with the same architecture.The generator module of GAN was constructed based on the U-net derivative network and residual blocks such that the high-level feature information could be extracted effectively via referring to the low-level feature information to avoid the loss of the detail information.In the meantime, the discriminator module could be demonstrated on the basis of fully convolutional neural network such that the pixel-level classification could be achieved to improve the discrimination accuracy.Besides, in order to improve the denoising ability and retain the image detail as much as possible, the composite loss function could be depicted by the illustrated denoising network based on the following three loss measures, adversarial loss, visual perception loss, and mean square error (MSE).Finally, the resultant three-channel output information could be fused by exploiting the arithmetic mean method to obtain the final denoised image.Compared with the state-of-the-art algorithms, experimental results show that the proposed algorithm can remove the image noise effectively and restore the original image details considerably.…”
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Caricature-visual face recognition based on jigsaw solving and modal decoupling by Yajun Yao, Chongwen Wang

“…The CVF-JSM consists of two modules: feature extraction and decoupling. The feature extraction module incorporates a graph attention network at the intermediate stage of the backbone network, which constructs and solves jigsaw puzzles to enable the network to extract shape features. …”
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SCite‐HRNet: A self‐calibrating efficient network for pose estimation by Nan Xiang, Xingdi Rao, Wenjing Yang, Jin Chen, Lifang Zhu

“…Ultimately, these two methodologies are incorporated into a multi‐scale information aggregation module and embed this module into the high‐resolution network. …”
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Medical image segmentation model based on local enhancement driven global optimization by Lianghui Xu, Ayiguli Halike, Gan Sen, Mo Sha

“…Abstract In medical image segmentation, it is a challenging task to identify and locate the boundary of pathological tissue accurately. In response to this issue, this paper proposes a medical image segmentation model, named Local Enhancement Driven Global Optimization Network (LEGO-Net), and specially develops an Detail and Contour Recognition Module (DCRM) to accurately identify the boundaries of lesion tissue. …”
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Dual-Branch Occlusion-Aware Semantic Part-Features Extraction Network for Occluded Person Re-Identification by Bo Sun, Yulong Zhang, Jianan Wang, Chunmao Jiang

“…Occlusion remains a major challenge in person re-identification, as it often leads to incomplete or misleading visual cues. To address this issue, we propose a dual-branch occlusion-aware network (DOAN), which explicitly and implicitly enhances the model’s capability to perceive and handle occlusions. …”
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YOLOv8-POS: a lightweight model for coal-rock image recognition by Yanqin Zhao, Wenyu Wang

“…The methodology introduces a C2f-PConv module, which ingeniously combines the strengths of C2f and partial convolution (PConv) to selectively process channels. …”
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Enhancing Point Cloud Classification and Segmentation With Attention-Enhanced SO-PointNet++ by Gang Cheng, Chengwei Gu

“…This module reduces the effect of the issue of gradient vanishing. …”
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CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution by Weiliang Liang, Yuan Liu

“…The feature extraction module integrates channel and spatial attention into the residual network, enabling the extraction of informative spectral and spatial features from the fused inputs. …”
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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land by Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang

“…Subsequently, a novel model of edge-enhanced dynamic graph convolutional network (GCN) (EDG-Net) was proposed to learn the discriminative features for classification of mining land with irregular edges, different sizes, a relatively small proportion, and sparse spatial distribution. (1) Edge-enhanced multiscale attention module: it is designed to capture key multiscale features and edge details using parallel dilated convolutions with attention fusion and edge enhancement, which facilitates the identification of objects with irregular edges and different sizes. (2) Downsampling fusion module: it integrates the features obtained through spatially split learning and max-pooling to overcome the information loss issue of small objects. (3) Patch-based dynamic GCN: the input images were split into several patches as nodes, and a graph was constructed and dynamically updated by connecting the nearest neighbors. …”
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Deep Learning-Based Instance Segmentation of Galloping High-Speed Railway Overhead Contact System Conductors in Video Images by Xiaotong Yao, Huayu Yuan, Shanpeng Zhao, Wei Tian, Dongzhao Han, Xiaoping Li, Feng Wang, Sihua Wang

“…When combined with the galloping visualization module, it can issue real-time alerts of conductor galloping anomalies, providing robust technical support for railway OCS safety monitoring.…”
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SVD-Based Feature Reconstruction Metric Network With Active Contrast Loss for Few-Shot SAR Target Recognition by Jia Zheng, Ming Li, Xiang Li, Peng Zhang, Yan Wu

“…However, in practical SAR applications, the scarcity of samples has always been a persistent issue, especially in the military domain, where certain target types may have very few samples. …”
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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…It mainly consists of a patchwise convolutional module (PTConv), pixelwise convolutional module (PXConv), residual cross-attention tokenization module (RCTM), and transformer feature fusion module (TFFM). …”
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Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. by Lingbo Li, Runpu Wang, Miaojie Zou, Fusen Guo, Yuheng Ren

“…At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. …”
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