| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Junwei Yu, Weiwei Chen, Nan Liu, Chao Fa. Oriented feature pyramid network for small and dense wheat heads detection and counting. Scientific reports. vol 14. issue 1. 2024-04-08. PMID:38582913. |
furthermore, we incorporate a path-aggregation and balanced feature pyramid network into our architecture to effectively extract both semantic and positional information from the input images. |
2024-04-08 |
2024-04-10 |
Not clear |
| Zhiwang Zhou, Yuanchang Zheng, Xiaoyu Zhou, Jie Yu, Shangjie Ron. Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network. BMC ophthalmology. vol 24. issue 1. 2024-03-04. PMID:38438876. |
in this paper, to conquer such issues, we first design a novel attention-aware segmentation model equipped with the multi-scale attention module in the pyramid structure-like encoder-decoder network, which can efficiently learn the global semantics and the long-range dependencies of the input images. |
2024-03-04 |
2024-03-07 |
Not clear |
| Kaifeng Ma, Mengshu Hao, Wenlong Shang, Jinping Liu, Junzhen Meng, Qingfeng Hu, Peipei He, Shiming L. Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models. Sensors (Basel, Switzerland). vol 24. issue 4. 2024-02-24. PMID:38400225. |
four semantic segmentation network models (ssnms), u-net, high-resolution net (hrnet)v2, pyramid scene parsing network (pspnet) and deeplabv3+, were used for learning and training. |
2024-02-24 |
2024-02-26 |
Not clear |
| Yang Hao, Tao Tang, Chunhai Ga. Train Distance Estimation for Virtual Coupling Based on Monocular Vision. Sensors (Basel, Switzerland). vol 24. issue 4. 2024-02-24. PMID:38400336. |
first, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. |
2024-02-24 |
2024-02-26 |
Not clear |
| Dong Kong, Xu Li, Qimin Xu, Yue Hu, Peizhou N. SC_LPR: Semantically Consistent LiDAR Place Recognition Based on Chained Cascade Network in Long-Term Dynamic Environments. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-02-23. PMID:38393840. |
to this end, we suggest a novel semantically consistent lidar pr method based on chained cascade network, called sc_lpr, which mainly consists of a lidar semantic image inpainting network (lsi-net) and a semantic pyramid transformer-based pr network (spt-net). |
2024-02-23 |
2024-02-26 |
Not clear |
| Dong Kong, Xu Li, Qimin Xu, Yue Hu, Peizhou N. SC_LPR: Semantically Consistent LiDAR Place Recognition Based on Chained Cascade Network in Long-Term Dynamic Environments. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-02-23. PMID:38393840. |
sequentially, in order to generate a discriminative global descriptor representing the point cloud, we design an encoder with pyramid transformer block to efficiently encode long-range dependencies and global contexts between different categories in the inpainted semantic image, followed by an augmented netvald, a generalized vlad (vector of locally aggregated descriptors) layer that adaptively aggregates salient local features. |
2024-02-23 |
2024-02-26 |
Not clear |
| Zhaojun Pang, Rongming Hu, Wu Zhu, Renyi Zhu, Yuxin Liao, Xiying Ha. A Building Extraction Method for High-Resolution Remote Sensing Images with Multiple Attentions and Parallel Encoders Combining Enhanced Spectral Information. Sensors (Basel, Switzerland). vol 24. issue 3. 2024-02-10. PMID:38339723. |
according to the different depth positions of the network, coordinate attention (ca) and convolutional block attention module (cbam) are introduced to bridge the encoder and decoder to retain richer spatial and semantic information during the encoding process, and adding the dense atrous spatial pyramid pooling (denseaspp) captures multi-scale contextual information during the upsampling of the layers of the decoder. |
2024-02-10 |
2024-02-12 |
Not clear |