{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:48:56Z","timestamp":1742921336332,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784899"},{"type":"electronic","value":"9789819784905"}],"license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-8490-5_16","type":"book-chapter","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:09:07Z","timestamp":1730884147000},"page":"218-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Local Spatial and\u00a0Global Context Activation for\u00a0Visual Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5168-3978","authenticated-orcid":false,"given":"Yunfei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Lijun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Junran","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"16_CR1","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omni Press, Haifa, Israel, pp. 807\u2013814 (2010)"},{"key":"16_CR2","unstructured":"Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016, Conference Track Proceedings (2016)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"16_CR4","unstructured":"Hendrycks, D., Gimpel, K.: Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units (2016). arxiv: CoRR abs\/1606.08415"},{"key":"16_CR5","unstructured":"Ramachandran, P., Zoph, B., Le, Q.V.: Searching for Activation Functions (2017). ArXiv:abs\/1710.05941"},{"key":"16_CR6","unstructured":"Misra D (2020) Mish: a self regularized non-monotonic activation function. In: 31st British Machine Vision Conference 2020, BMVC 2020, Virtual Event, UK, 7\u201310 Sept. 2020. BMVA Press"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable Convolutional Networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Wavelets: Time-Frequency Methods and Phase Space Proceedings of the International Conference, Marseille, France, 14\u201318 Dec. 1987, pp. 286\u2013297. Springer (1990)","DOI":"10.1007\/978-3-642-75988-8_28"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Sun, J.: Funnel activation for visual recognition. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 Aug. 2020, Proceedings, Part XI 16, pp. 351\u2013368. Springer (2020)","DOI":"10.1007\/978-3-030-58621-8_21"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"16_CR11","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2014). arXiv:1409.1556"},{"key":"16_CR12","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y., others: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, Atlanta, GA, p. 3 (2013)"},{"key":"16_CR13","unstructured":"Xu, B., Wang, N., Chen, T., Li, M.: Empirical Evaluation of Rectified Activations in Convolutional Network (2015). arXiv:1505.00853"},{"key":"16_CR14","doi-asserted-by":"publisher","unstructured":"Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K.: Smooth maximum unit: smooth activation function for deep networks using smoothing maximum technique. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 784\u2013793 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00087","DOI":"10.1109\/CVPR52688.2022.00087"},{"key":"16_CR15","unstructured":"Cai, H., Zhu, L., Han, S.: Proxylessnas: Direct Neural Architecture Search on Target Task and Hardware (2018). arXiv:1812.00332"},{"key":"16_CR16","unstructured":"Iu, H., Simonyan, K., Yang, Y.: Darts: Differentiable Architecture Search (2018). arXiv:1806.09055"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Lu, Z., Deb, K., Goodman, E., Banzhaf, W., Boddeti, V.N.: Nsganetv2: Evolutionary multi-objective surrogate-assisted neural architecture search. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 Aug. 2020, Proceedings, Part I 16, pp. 35\u201351. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_3"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Wang, D., Li, M., Gong, C., Chandra, V.: (2021) Attentivenas: improving neural architecture search via attentive sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6418\u20136427","DOI":"10.1109\/CVPR46437.2021.00635"},{"key":"16_CR19","unstructured":"Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: International Conference on Machine Learning. PMLR, pp. 1319\u20131327 (2013)"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Liu, M., Sun, J.: Activate or not: Learning customized activation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8032\u20138042 (2021)","DOI":"10.1109\/CVPR46437.2021.00794"},{"key":"16_CR21","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving Neural Networks by Preventing Co-adaptation of Feature Detectors (2012). arXiv:1207.0580"},{"key":"16_CR22","first-page":"12075","volume":"2021","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Zhu, Z., Zhong, Z.: Learning specialized activation functions with the Piecewise Linear Unit. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV) 2021, 12075\u201312084 (2021)","journal-title":"IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic relu. In: European Conference on Computer Vision, pp. 351\u2013367. Springer (2020)","DOI":"10.1007\/978-3-030-58529-7_21"},{"key":"16_CR24","doi-asserted-by":"publisher","unstructured":"Cai, S.: IIEU: rethinking neural feature activation from decision-making. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, pp. 5773\u20135783 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.00533","DOI":"10.1109\/ICCV51070.2023.00533"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"16_CR27","unstructured":"Yu, F., Koltun, V.: Multi-scale Context Aggregation by Dilated Convolutions. arXiv:1511.07122"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp 0\u20130 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"16_CR30","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. PMLR, pp. 448\u2013456 (2015)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5353\u20135360 (2015)","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"16_CR32","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Visualizing the loss landscape of neural nets. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR34","unstructured":"Contributors Mmc. (2020). OpenMMLab\u2019s Image Classification Toolbox and Benchmark. https:\/\/github.com\/open-mmlab\/mmclassification"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, 6\u201312 Sept. 2014, Proceedings, Part V 13, pp. 740\u2013755. Springer (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8490-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:14:16Z","timestamp":1730884456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8490-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"ISBN":["9789819784899","9789819784905"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8490-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"7 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}