| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Shan Zhao, Xin Zhao, Zhanqiang Huo, Fukai Zhan. BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39204841. |
bmsenet: multiscale context pyramid pooling and spatial detail enhancement network for real-time semantic segmentation. |
2024-08-29 |
2024-09-04 |
Not clear |
| Shan Zhao, Xin Zhao, Zhanqiang Huo, Fukai Zhan. BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39204841. |
first, to address the limitation of singular semantic feature scales, a multiscale context pyramid pooling module (mscppm) is introduced. |
2024-08-29 |
2024-09-04 |
Not clear |
| Shan Zhao, Zihao Wang, Zhanqiang Huo, Fukai Zhan. Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion Utilizing a Multi-Scale Dilated Convolutional Pyramid. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39205000. |
semantic segmentation network based on adaptive attention and deep fusion utilizing a multi-scale dilated convolutional pyramid. |
2024-08-29 |
2024-09-04 |
Not clear |
| Shan Zhao, Zihao Wang, Zhanqiang Huo, Fukai Zhan. Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion Utilizing a Multi-Scale Dilated Convolutional Pyramid. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39205000. |
to address these challenges, this paper proposes a semantic segmentation network based on adaptive attention and deep fusion with the multi-scale dilated convolutional pyramid (sdamnet). |
2024-08-29 |
2024-09-04 |
Not clear |
| Shan Zhao, Zihao Wang, Zhanqiang Huo, Fukai Zhan. Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion Utilizing a Multi-Scale Dilated Convolutional Pyramid. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39205000. |
specifically, the dilated convolutional atrous spatial pyramid pooling (dcaspp) module is developed to enhance contextual information in semantic segmentation. |
2024-08-29 |
2024-09-04 |
Not clear |
| Wenbin Chen, Zheng Wang, Hongchao Zha. PCB plug-in solder joint defect detection method based on coordinated attention-guided information fusion. Scientific reports. vol 14. issue 1. 2024-08-27. PMID:39191831. |
firstly, on the basis of yolov3, an attention-guided pyramid structure is used to fuse context information, and multiple convolutions of different size are used to explore richer high-level semantic information; secondly, a coordinated attention network structure is introduced to calibrate the fused pyramidal feature information, highlighting the important feature channels, and reducing the adverse impact of redundant parameters generated by feature fusion; finally, the aspp (atrous spatial pyramid pooling) structure is implemented in the original darknet53 backbone feature extraction network to acquire multi-scale feature information of the detection targets. |
2024-08-27 |
2024-08-30 |
Not clear |
| Guojun Chen, Yongjie Hou, Haozhen Chen, Lei Cao, Jianqiang Yua. A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation: leveraging dilated residual and multi-screening path aggregation. Frontiers in plant science. vol 15. 2024-08-07. PMID:39109070. |
next, we designed a multilevel scale fusion feature pyramid network (hs-pan) to enrich semantic information and strengthen localization information to enhance the detection of color-changing melon fruits with different maturity levels. |
2024-08-07 |
2024-08-09 |
Not clear |
| Peirui Zhao, Wenhua Zhou, Li N. High-precision object detection network for automate pear picking. Scientific reports. vol 14. issue 1. 2024-06-28. PMID:38942940. |
the proposed, high-level deformation-perception network with multi-object search nms(hdmnet), is based on yolov8 and utilizes a high-level semantic focused attention mechanism module to eliminate irrelevant background information and a deformation-perception feature pyramid network to improve accuracy of long-distance and small scale fruit. |
2024-06-28 |
2024-07-01 |
Not clear |
| Yuanhang Jin, Xiaosheng Liu, Xiaobin Huan. EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images. Sensors (Basel, Switzerland). vol 24. issue 11. 2024-06-19. PMID:38894469. |
furthermore, this paper integrates a refconv and multi-dconv head transposed attention (rma) feature pyramid structure into the hrnet model, augmenting the model's capacity for semantic recognition and expression at various levels. |
2024-06-19 |
2024-06-21 |
human |
| Huaqiang Zhang, Chenggang Dai, Chengjun Chen, Zhengxu Zhao, Mingxing Li. One stage multi-scale efficient network for underwater target detection. The Review of scientific instruments. vol 95. issue 6. 2024-06-18. PMID:38888402. |
by integrating low-level features with high-level features, the adaptive fusion feature pyramid network effectively integrates global semantic information and decreases the semantic gap between features from various layers, contributing to the high detection precision. |
2024-06-18 |
2024-06-21 |
Not clear |
| Jintao Wang, Mao Qi, Zhenwu Xiang, Yi Tian, Dongbing Ton. SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation. Computers in biology and medicine. vol 177. 2024-06-11. PMID:38815486. |
the proposed model incorporates features such as the u-net backbone, semantic aggregation feature pyramid module, and reverse attention module. |
2024-06-11 |
2024-06-14 |
Not clear |
| Baohua Huang, Aokun Bai, Yuqiong Wu, Chanjuan Yang, Han Su. DB-EAC and LSTR: DBnet based seal text detection and Lightweight Seal Text Recognition. PloS one. vol 19. issue 5. 2024-05-16. PMID:38753628. |
the efficient channel attention module is added to the differentiable binarization network to solve the feature pyramid conflict, and the convolutional layer network structure is improved to delay downsampling for reducing semantic feature loss. |
2024-05-16 |
2024-05-27 |
Not clear |
| Chengyi Zou, Shuai Wan, Marc Gorriz Blanch, Luka Murn, Marta Mrak, Juil Sock, Fei Yang, Luis Herran. Lightweight Deep Exemplar Colorization via Semantic Attention-Guided Laplacian Pyramid. IEEE transactions on visualization and computer graphics. vol PP. 2024-05-10. PMID:38722720. |
lightweight deep exemplar colorization via semantic attention-guided laplacian pyramid. |
2024-05-10 |
2024-05-27 |
Not clear |
| Chengyi Zou, Shuai Wan, Marc Gorriz Blanch, Luka Murn, Marta Mrak, Juil Sock, Fei Yang, Luis Herran. Lightweight Deep Exemplar Colorization via Semantic Attention-Guided Laplacian Pyramid. IEEE transactions on visualization and computer graphics. vol PP. 2024-05-10. PMID:38722720. |
to address these problems, this paper proposes a lightweight semantic attention-guided laplacian pyramid network (saglp-net) for deep exemplar-based colorization, exploiting the inherent multi-scale properties of color representations. |
2024-05-10 |
2024-05-27 |
Not clear |
| Chengyi Zou, Shuai Wan, Marc Gorriz Blanch, Luka Murn, Marta Mrak, Juil Sock, Fei Yang, Luis Herran. Lightweight Deep Exemplar Colorization via Semantic Attention-Guided Laplacian Pyramid. IEEE transactions on visualization and computer graphics. vol PP. 2024-05-10. PMID:38722720. |
they are exploited through a laplacian pyramid, and semantic information is introduced as high-level guidance to align the object and background information. |
2024-05-10 |
2024-05-27 |
Not clear |
| Dan Shan, Zhi Yang, Xiaofeng Wang, Xiangdong Meng, Guangwei Zhan. An Aerial Image Detection Algorithm Based on Improved YOLOv5. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676234. |
finally, we replace the original pan + fpn network structure with the optimized bifpn (bidirectional feature pyramid network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. |
2024-04-27 |
2024-04-29 |
Not clear |
| Yong Wang, Panxing Zhang, Shuang Tia. Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion. Frontiers in plant science. vol 15. 2024-04-26. PMID:38654901. |
finally, the birepgfpn replaces the path aggregation feature pyramid network (pafpn) in the yolov6 model to achieve effective fusion of deep semantic and shallow spatial information. |
2024-04-26 |
2024-04-28 |
Not clear |
| Honggui Han, Qiyu Zhang, Fangyu Li, Yongping D. Foreground Capture Feature Pyramid Network-Oriented Object Detection in Complex Backgrounds. IEEE transactions on neural networks and learning systems. vol PP. 2024-04-22. PMID:38648132. |
then, the pa module adaptively learns the fusion weights of multiscale features at different levels of the feature pyramid, which enhances the complementarity of semantic information between different levels of the foreground feature maps. |
2024-04-22 |
2024-04-26 |
Not clear |
| Huifeng Su, David Bonfils Kamanda, Tao Han, Cheng Guo, Rongzhao Li, Zhilei Liu, Fengzhao Su, Liuhong Shan. Enhanced YOLO v3 for precise detection of apparent damage on bridges amidst complex backgrounds. Scientific reports. vol 14. issue 1. 2024-04-15. PMID:38622182. |
first, the yolo v3 network structure is enhanced to better accommodate the dense distribution and large variation of disease scale characteristics, and the detection layer incorporates the squeeze and excitation (se) networks attention mechanism module and spatial pyramid pooling module to strengthen the semantic feature extraction ability. |
2024-04-15 |
2024-04-18 |
Not clear |
| Nuo Xu, Zhibin Ma, Yi Xia, Yanqi Dong, Jiali Zi, Delong Xu, Fu Xu, Xiaohui Su, Haiyan Zhang, Feixiang Che. A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification. Animals : an open access journal from MDPI. vol 14. issue 7. 2024-04-13. PMID:38612345. |
specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. |
2024-04-13 |
2024-04-15 |
Not clear |