{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:05:03Z","timestamp":1768989903456,"version":"3.49.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030678340","type":"print"},{"value":"9783030678357","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67835-7_12","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T07:44:49Z","timestamp":1617263089000},"page":"134-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Median-Pooling Grad-CAM: An Efficient Inference Level Visual Explanation for CNN Networks in Remote Sensing Image Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-5563","authenticated-orcid":false,"given":"Wei","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyuan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinling","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liotta","family":"Antonio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"12_CR1","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Yang Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: ACM GIS 2010, p. 270. ACM, San Jose (2010)","DOI":"10.1145\/1869790.1869829"},{"issue":"11","key":"12_CR3","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"12_CR4","unstructured":"Molnar, C.: Interpretable machine learning - a guide for making black box models explainable. https:\/\/christophm.github.io\/. Accessed 21 Feb 2020"},{"key":"12_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"12_CR6","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: Proceedings of International Conference on Learning Representations (2015)"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., Guestrin, C.: Why should I trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"12_CR8","unstructured":"Zhang, H., Chen, J., Xue, H., Zhang, Q.: Towards a unified evaluation of explanation methods without ground truth. arXiv:1911.09017 (2019)"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Oliva, L. A., A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of CVPR, pp. 2921\u20132929. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Fukui H., Hirakawa T., Yamashita T., et al.: Attention branch network: learning of attention mechanism for visual explanation. In: Proceedings of 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10697\u201310706. IEEE (2019)","DOI":"10.1109\/CVPR.2019.01096"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 ICCV, pp. 618\u2013626. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: WACV, pp. 839\u2013847. IEEE, Lake Tahoe (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"12_CR13","unstructured":"Omeiza, D., Speakman, S., Cintas C., Weldemariam, K.: Smooth grad-CAM++: an enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224v1 (2019)"},{"key":"12_CR14","unstructured":"Smilkov, D., Thorat, N., Kim, B., et al.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825v1 (2017)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Song, W., Dai, S.Y., Wang, J., Huang, D., Liotta, A., Di Fatta, G.: Bi-gradient verification for grad-CAM towards accurate visual explanation for remote sensing images. In: 2019 ICDMW, pp. 473\u2013479, Beijing, China (2019)","DOI":"10.1109\/ICDMW.2019.00074"},{"issue":"7","key":"12_CR16","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TMM.2019.2949872","volume":"22","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Su, H., Zhang, B., Hu, X.: Learning reliable visual saliency for model explanations. IEEE Trans. Multimed. 22(7), 1796\u20131807 (2020)","journal-title":"IEEE Trans. Multimed."},{"key":"12_CR17","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., et al.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, pp. 9505\u20139515. Curran Associates, Inc. (2018)"},{"key":"12_CR18","unstructured":"Yang M., Kim, B.: Benchmarking attribution methods with relative feature importance. arXiv preprint arXiv:1907.09701 (2019)"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 CVPR, pp. 770\u2013778. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"12_CR20","doi-asserted-by":"publisher","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","volume":"54","author":"B Zhao","year":"2016","unstructured":"Zhao, B., Zhong, Y., Xia, G.S., Zhang, L.: Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 54(4), 2108\u20132123 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67835-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T07:14:15Z","timestamp":1617779655000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-67835-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030678340","9783030678357"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67835-7_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2021.cz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"211","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":"73","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":"35% - 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":"2,63","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":"2,5","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)"}}]}}