SCite‐HRNet: A self‐calibrating efficient network for pose estimation
Abstract In order to tackle the challenge of capturing long‐range spatial dependencies among joints, a novel self‐calibrated lightweight high‐resolution network (SCite‐HRNet), which is grounded on the lightweight high‐resolution network is introduced. A self‐calibrated segmentation convolution is fi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.70106 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850108607197085696 |
|---|---|
| author | Nan Xiang Xingdi Rao Wenjing Yang Jin Chen Lifang Zhu |
| author_facet | Nan Xiang Xingdi Rao Wenjing Yang Jin Chen Lifang Zhu |
| author_sort | Nan Xiang |
| collection | DOAJ |
| description | Abstract In order to tackle the challenge of capturing long‐range spatial dependencies among joints, a novel self‐calibrated lightweight high‐resolution network (SCite‐HRNet), which is grounded on the lightweight high‐resolution network is introduced. A self‐calibrated segmentation convolution is first designed to extract and amalgamate contextual information across various scales, thereby addressing the issue of excessive computation engendered by stacked convolution kernels in conventional convolution methods. Next, a multi‐scale channel attention mechanism designed to extract structural information while filtering out unnecessary channel details is introduced. Ultimately, these two methodologies are incorporated into a multi‐scale information aggregation module and embed this module into the high‐resolution network. This allows the network to maintain exceptionally efficient computation while effectively managing information across various scales. Empirical results indicate that SCite‐HRNet achieves remarkable performance on both the COCO dataset and the challenging average precision (AP)‐10K dataset. |
| format | Article |
| id | doaj-art-4bf5be0719cb423e87a1c4c59e4c0239 |
| institution | OA Journals |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-4bf5be0719cb423e87a1c4c59e4c02392025-08-20T02:38:18ZengWileyElectronics Letters0013-51941350-911X2024-12-016023n/an/a10.1049/ell2.70106SCite‐HRNet: A self‐calibrating efficient network for pose estimationNan Xiang0Xingdi Rao1Wenjing Yang2Jin Chen3Lifang Zhu4Liangjiang International College Chongqing University of Technology Chongqing ChinaLiangjiang Artificial Intelligence College Chongqing University of Technology Chongqing ChinaComputer Science and Engineering College Chongqing University of Technology Chongqing ChinaChongqing Jialing Special Equipment Co., Ltd. Chongqing ChinaChongqing Jialing Special Equipment Co., Ltd. Chongqing ChinaAbstract In order to tackle the challenge of capturing long‐range spatial dependencies among joints, a novel self‐calibrated lightweight high‐resolution network (SCite‐HRNet), which is grounded on the lightweight high‐resolution network is introduced. A self‐calibrated segmentation convolution is first designed to extract and amalgamate contextual information across various scales, thereby addressing the issue of excessive computation engendered by stacked convolution kernels in conventional convolution methods. Next, a multi‐scale channel attention mechanism designed to extract structural information while filtering out unnecessary channel details is introduced. Ultimately, these two methodologies are incorporated into a multi‐scale information aggregation module and embed this module into the high‐resolution network. This allows the network to maintain exceptionally efficient computation while effectively managing information across various scales. Empirical results indicate that SCite‐HRNet achieves remarkable performance on both the COCO dataset and the challenging average precision (AP)‐10K dataset.https://doi.org/10.1049/ell2.70106artificial intelligencecomputer visionconvolutionimage classification |
| spellingShingle | Nan Xiang Xingdi Rao Wenjing Yang Jin Chen Lifang Zhu SCite‐HRNet: A self‐calibrating efficient network for pose estimation Electronics Letters artificial intelligence computer vision convolution image classification |
| title | SCite‐HRNet: A self‐calibrating efficient network for pose estimation |
| title_full | SCite‐HRNet: A self‐calibrating efficient network for pose estimation |
| title_fullStr | SCite‐HRNet: A self‐calibrating efficient network for pose estimation |
| title_full_unstemmed | SCite‐HRNet: A self‐calibrating efficient network for pose estimation |
| title_short | SCite‐HRNet: A self‐calibrating efficient network for pose estimation |
| title_sort | scite hrnet a self calibrating efficient network for pose estimation |
| topic | artificial intelligence computer vision convolution image classification |
| url | https://doi.org/10.1049/ell2.70106 |
| work_keys_str_mv | AT nanxiang scitehrnetaselfcalibratingefficientnetworkforposeestimation AT xingdirao scitehrnetaselfcalibratingefficientnetworkforposeestimation AT wenjingyang scitehrnetaselfcalibratingefficientnetworkforposeestimation AT jinchen scitehrnetaselfcalibratingefficientnetworkforposeestimation AT lifangzhu scitehrnetaselfcalibratingefficientnetworkforposeestimation |