| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Mengzi Hu, Ziyang Li, Jiong Yu, Xueqiang Wan, Haotian Tan, Zeyu Li. Efficient-Lightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images. Sensors (Basel, Switzerland). vol 23. issue 14. 2023-07-29. PMID:37514717. |
secondly, we design efficient spatial pyramid pooling (espp) to augment the representation of small-object features in aerial images. |
2023-07-29 |
2023-08-14 |
Not clear |
| Dayu Tan, Yansen Su, Xin Peng, Hongtian Chen, Chunhou Zheng, Xingyi Zhang, Weimin Zhon. Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism. IEEE transactions on cybernetics. vol PP. 2023-06-06. PMID:37279140. |
in order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a dense-attention module (pyramid spatial channel (psc) attention) to perform feature recalibration, and through the psc attention to model the interdependence among convolution feature channels. |
2023-06-06 |
2023-08-14 |
Not clear |
| Erjie Su, Yongzhi Tian, Erjun Liang, Jiayu Wang, Yibo Zhan. A Multiscale Instance Segmentation Method Based on Cleaning Rubber Ball Images. Sensors (Basel, Switzerland). vol 23. issue 9. 2023-05-14. PMID:37177464. |
first, we introduce the pyramid vision transformer instead of the convolution module in the backbone network and use the spatial-reduction attention layer of the transformer to improve the feature extraction ability across scales and spatial reduction to reduce computational cost; second, we improve the feature fusion module to fuse image features across scales, combined with an attention mechanism to enhance the output feature representation; third, the prediction head separates the mask branches separately. |
2023-05-14 |
2023-08-14 |
Not clear |
| Wenxuan Liu, Xian Zhong, Zhuo Zhou, Kui Jiang, Zheng Wang, Chia-Wen Li. Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-05-11. PMID:37163393. |
2) for the view, we utilize a pyramid dynamic recommendation (pdr) to learn a complete and detailed global representation by transferring features from different views. |
2023-05-11 |
2023-08-14 |
Not clear |
| Dongfang Liu, James Liang, Tony Geng, Alexander Loui, Tianfei Zho. Tripartite Feature Enhanced Pyramid Network for Dense Prediction. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-05-09. PMID:37155388. |
third, we introduce a feature feedback module in fpn, which creates a communication channel from the feature pyramid back to the bottom-up backbone and doubles the encoding capacity, enabling the entire architecture to generate incrementally more powerful representations. |
2023-05-09 |
2023-08-14 |
Not clear |
| Lingling Du, Hanruo Liu, Lan Zhang, Yao Lu, Mengyao Li, Yang Hu, Yi Zhan. Deep ensemble learning for accurate retinal vessel segmentation. Computers in biology and medicine. vol 158. 2023-04-13. PMID:37054633. |
it evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision transformer and fcn-transformer. |
2023-04-13 |
2023-08-14 |
Not clear |
| L M Donini, E M Berry, A Pinto, A M Giusti, L Muzzioli, A Lenzi, E Poggiogall. The Sapienza count-down for a healthy and sustainable diet. Annali di igiene : medicina preventiva e di comunita. vol 35. issue 3. 2023-03-10. PMID:36896954. |
the mediterranean diet is normally proposed through a graphic depiction that represents a pyramid which is a simple and effective representation but not of immediate impact. |
2023-03-10 |
2023-08-14 |
Not clear |
| Wei Zhang, He Huang, Youqiang Sun, Xiaowei W. AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning. Frontiers in plant science. vol 13. 2023-01-02. PMID:36589124. |
secondly, a novel grouping spatial pyramid pooling fast (gsppf) is designed, which enriches the multi-scale representation of pest features |
2023-01-02 |
2023-08-14 |
Not clear |
| Yuze Li, Wushour Silamu, Zhenchao Wang, Miaomiao X. Attention-Based Scene Text Detection on Dual Feature Fusion. Sensors (Basel, Switzerland). vol 22. issue 23. 2022-12-11. PMID:36501774. |
the bi-directional feature fusion pyramid module (bfm) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. |
2022-12-11 |
2023-08-14 |
Not clear |
| Jian Sun, Hongwei Gao, Xuna Wang, Jiahui Y. Scale Enhancement Pyramid Network for Small Object Detection from UAV Images. Entropy (Basel, Switzerland). vol 24. issue 11. 2022-11-24. PMID:36421553. |
additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (fpn). |
2022-11-24 |
2023-08-14 |
Not clear |
| Jing Qi, Guangcong Ruan, Jia Liu, Yi Yang, Qian Cao, Yanling Wei, Yongjian Nia. PHF Bioengineering (Basel, Switzerland). vol 9. issue 11. 2022-11-10. PMID:36354543. |
specifically, the phf3 model has a dual-branch hybrid architecture with resnet50 and a pyramid vision transformer (pvt), where the local features extracted by resnet50 represent the relationship between the intestinal wall at the near-shot point and its depth, and the global representations modeled by the pvt capture similar information in the cross-section of the intestinal cavity. |
2022-11-10 |
2023-08-14 |
Not clear |
| Lu Zou, Zhangjin Huang, Naijie Gu, Guoping Wan. 6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-Based Instance Representation Learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2022-10-31. PMID:36315551. |
pixelformer contains a pyramid transformer encoder with an all-multilayer perceptron (mlp) decoder to extract pixelwise appearance representations from rgb images, while pointformer relies on a cascaded transformer encoder and an all-mlp decoder to acquire the pointwise geometric characteristics from point clouds. |
2022-10-31 |
2023-08-14 |
Not clear |
| Zhengshan Huang, Yu Guo, Ning Zhang, Xian Huang, Pierre Decazes, Stephanie Becker, Su Rua. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Computers in biology and medicine. vol 151. issue Pt A. 2022-10-28. PMID:36306574. |
a 3d v-net is used as the backbone of the segmentation network and image features extracted in different convolutional layers are fused with the atrous spatial pyramid pooling (aspp) module to generate multi-scale feature representations of input images. |
2022-10-28 |
2023-08-14 |
Not clear |
| Xingqing Nie, Xiaogen Zhou, Tong Tong, Xingtao Lin, Luoyan Wang, Haonan Zheng, Jing Li, Ensheng Xue, Shun Chen, Meijuan Zheng, Cong Chen, Min D. N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation. Frontiers in neuroscience. vol 16. 2022-09-19. PMID:36117632. |
first, we apply the multi-scale input layer to construct an image pyramid, which achieves multi-level receiver field sizes and obtains rich feature representation. |
2022-09-19 |
2023-08-14 |
Not clear |
| Linsen Xie, Wentian Cai, Ying Ga. DMCGNet: A Novel Network for Medical Image Segmentation with Dense Self-Mimic and Channel Grouping Mechanism. IEEE journal of biomedical and health informatics. vol PP. 2022-08-08. PMID:35939480. |
specifically, we introduce a pyramid target-aware dense self mimic (ptdsm) module, which is capable of exploring deeper and better feature representation with no parameter increase. |
2022-08-08 |
2023-08-14 |
Not clear |
| Yuchun Li, Cong Lin, Yu Zhang, Siling Feng, Mengxing Huang, Zhiming Ba. Automatic Segmentation of Prostate MRI Based on 3D Pyramid Pooling Unet. Medical physics. 2022-08-04. PMID:35923153. |
in addition, pyramid pooling performs multi-faceted feature extraction on each image behind the convolutional layer, and deep supervision learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task. |
2022-08-04 |
2023-08-14 |
Not clear |
| Xiaoqing Zhang, Zunjie Xiao, Xiaoling Li, Xiao Wu, Hanxi Sun, Jin Yuan, Risa Higashita, Jiang Li. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health information science and systems. vol 10. issue 1. 2022-04-11. PMID:35401971. |
in the mpanet, we design a novel mixed pyramid attention (mpa) block, which first applies the group convolution method to enhance the feature representation difference of feature maps and then construct a mixed pyramid pooling structure to extract local-global feature representations and different feature representation types simultaneously. |
2022-04-11 |
2023-08-13 |
Not clear |
| Thanaporn Viriyasaranon, Jang-Hwan Cho. Object detectors involving a NAS-gate convolutional module and capsule attention module. Scientific reports. vol 12. issue 1. 2022-03-11. PMID:35273256. |
several state-of-the-art object detectors have demonstrated outstanding performances by optimizing feature representation through modification of the backbone architecture and exploitation of a feature pyramid. |
2022-03-11 |
2023-08-13 |
Not clear |
| Thanaporn Viriyasaranon, Jang-Hwan Cho. Object detectors involving a NAS-gate convolutional module and capsule attention module. Scientific reports. vol 12. issue 1. 2022-03-11. PMID:35273256. |
the capsule attention module exploits the strong spatial relationship encoding ability of the capsule network to generate a spatial attention mask, which emphasizes important features and suppresses unnecessary features in the feature pyramid, in order to optimize the feature representation and localization capability of the detectors. |
2022-03-11 |
2023-08-13 |
Not clear |
| Udit Sharma, Bruno Artacho, Andreas Savaki. GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention. Sensors (Basel, Switzerland). vol 21. issue 22. 2021-11-30. PMID:34833577. |
the refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. |
2021-11-30 |
2023-08-13 |
Not clear |