{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:15:01Z","timestamp":1743009301175,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981403"},{"type":"electronic","value":"9789819981410"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8141-0_15","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"189-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GoatPose: A Lightweight and\u00a0Efficient Network with\u00a0Attention Mechanism"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3149-4654","authenticated-orcid":false,"given":"Yaxuan","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1833-0617","authenticated-orcid":false,"given":"Annan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-6586","authenticated-orcid":false,"given":"Shengxi","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2017)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"15_CR2","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). http:\/\/arxiv.org\/abs\/1704.04861, cite arxiv:1704.04861"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for mobilenetv3. In: ICCV, pp. 1314\u20131324. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"15_CR6","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding (2016)"},{"key":"15_CR7","unstructured":"Lin, J., Rao, Y., Lu, J., Zhou, J.: Runtime neural pruning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 2178\u20132188, Red Hook, NY, USA. Curran Associates Inc. (2017)"},{"key":"15_CR8","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks (2016)"},{"key":"15_CR9","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. NIPS 2015, Cambridge, MA, USA. MIT Press (2015)"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: more features from cheap operations (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Han, K., et al.: GhostNets on heterogeneous devices via cheap operations. Int. J. Comput. Vis. 130(4), 1050\u20131069 (2022). https:\/\/doi.org\/10.1007\/s11263-022-01575-y, https:\/\/doi.org\/10.10072Fs11263-022-01575-y","DOI":"10.1007\/s11263-022-01575-y"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks (2019)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"15_CR14","unstructured":"Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: BAM: bottleneck attention module (2018)"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context (2015)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation (2016)","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"15_CR18","unstructured":"Badrinarayanan, V., Handa, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling (2015)"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Peng, X., Feris, R.S., Wang, X., Metaxas, D.N.: A recurrent encoder-decoder network for sequential face alignment (2016)","DOI":"10.1007\/978-3-319-46448-0_3"},{"key":"15_CR23","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2016)"},{"key":"15_CR24","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs (2016)"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"15_CR26","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation (2017)"},{"key":"15_CR27","unstructured":"Chen, L.C., et al.: Searching for efficient multi-scale architectures for dense image prediction (2018)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Auto-deepLab: hierarchical neural architecture search for semantic image segmentation (2019)","DOI":"10.1109\/CVPR.2019.00017"},{"key":"15_CR29","unstructured":"Yang, T.J., et al.: Deeperlab: single-shot image parser (2019)"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: Panoptic-deeplab: a simple, strong, and fast baseline for bottom-up panoptic segmentation (2020)","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"15_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126301","volume":"544","author":"Y Niu","year":"2023","unstructured":"Niu, Y., Wang, A., Wang, X., Wu, S.: Convpose: a modern pure convnet for human pose estimation. Neurocomputing 544, 126301 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.126301","journal-title":"Neurocomputing"},{"key":"15_CR32","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition (2020)"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks (2019)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"15_CR35","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017)"},{"key":"15_CR36","unstructured":"Sun, K., Li, M., Liu, D., Wang, J.: Igcv 3: interleaved low-rank group convolutions for efficient deep neural networks (2018)"},{"key":"15_CR37","unstructured":"Tan, M., Le, Q.V.: Mixconv: mixed depthwise convolutional kernels (2019)"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Neff, C., Sheth, A., Furgurson, S., Tabkhi, H.: EfficienthrNet: efficient scaling for lightweight high-resolution multi-person pose estimation (2020)","DOI":"10.1007\/s11554-021-01132-9"},{"key":"15_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, L., Wu, W., Qian, C., Lu, T.: Tam: temporal adaptive module for video recognition (2021)","DOI":"10.1109\/ICCV48922.2021.01345"},{"key":"15_CR40","unstructured":"Liu, Y., Shao, Z., Teng, Y., Hoffmann, N.: Nam: normalization-based attention module (2021)"},{"key":"15_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Tokenpose: learning keypoint tokens for human pose estimation (2021)","DOI":"10.1109\/ICCV48922.2021.01112"},{"key":"15_CR42","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhou, Y., Xu, F.: Transpose: real-time 3D human translation and pose estimation with six inertial sensors (2021)","DOI":"10.1145\/3450626.3459786"},{"key":"15_CR43","unstructured":"Yuan, Y., et al.: Hrformer: high-resolution transformer for dense prediction (2021)"},{"key":"15_CR44","unstructured":"Xu, Y., Zhang, J., Zhang, Q., Tao, D.: ViTPose: simple vision transformer baselines for human pose estimation (2022)"},{"key":"15_CR45","unstructured":"Jiang, T., et al.: RTMPose: real-time multi-person pose estimation based on MMPose (2023)"},{"key":"15_CR46","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zeng, A., Yuan, C., Li, Y.: Effective whole-body pose estimation with two-stages distillation (2023)","DOI":"10.1109\/ICCVW60793.2023.00455"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8141-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T09:26:42Z","timestamp":1730626002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8141-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981403","9789819981410"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8141-0_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}