{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T05:21:44Z","timestamp":1755926504710,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585648"},{"type":"electronic","value":"9783030585655"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58565-5_29","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T12:03:19Z","timestamp":1605096199000},"page":"481-497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["New Threats Against Object Detector with Non-local Block"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-4333","authenticated-orcid":false,"given":"Yi","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8582-1673","authenticated-orcid":false,"given":"Fan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9728-9511","authenticated-orcid":false,"given":"Adams Wai-Kin","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7479-7970","authenticated-orcid":false,"given":"Kwok-Yan","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60\u201365. IEEE (2005)","DOI":"10.1109\/CVPR.2005.38"},{"key":"29_CR2","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 International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L., Song, H., Li, Q., Cui, Y., Yang, J., Hu, X.T.: Liver segmentation in CT images using a non-local fully convolutional neural network. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 639\u2013642. IEEE (2019)","DOI":"10.1109\/BIBM47256.2019.8983303"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Chen, S.T., Cornelius, C., Martin, J., Chau, D.H.: ShapeShifter: robust physical adversarial attack on faster R-CNN object detector. In: ECML\/PKDD (2018)","DOI":"10.1007\/978-3-030-10925-7_4"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Chi, L., Tian, G., Mu, Y., Xie, L., Tian, Q.: Fast non-local neural networks with spectral residual learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 2142\u20132151 (2019)","DOI":"10.1145\/3343031.3351029"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Fu, C., et al.: Non-local recurrent neural memory for supervised sequence modeling. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6311\u20136320 (2019)","DOI":"10.1109\/ICCV.2019.00641"},{"key":"29_CR7","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015). http:\/\/arxiv.org\/abs\/1412.6572"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Hu, G., Cui, B., Yu, S.: Skeleton-based action recognition with synchronous local and non-local spatio-temporal learning and frequency attention. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1216\u20131221. IEEE (2019)","DOI":"10.1109\/ICME.2019.00212"},{"key":"29_CR10","unstructured":"Huang, Y., Kong, A.W.K., Lam, K.Y.: Adversarial signboard against object detector. In: Proceedings of the British Machine Vision Conference (BMVC) (2019)"},{"key":"29_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-29894-4_1","volume-title":"PRICAI 2019: Trends in Artificial Intelligence","author":"Y Huang","year":"2019","unstructured":"Huang, Y., Kong, A.W.-K., Lam, K.-Y.: Attacking object detectors without changing the target object. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11672, pp. 3\u201315. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29894-4_1"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, J.: Improving video captioning with non-local neural networks. In: 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), pp. 206\u2013212 (2018)","DOI":"10.1109\/ICCE-ASIA.2018.8552140"},{"key":"29_CR13","unstructured":"Levi, H., Ullman, S.: Efficient coarse-to-fine non-local module for the detection of small objects. arXiv preprint arXiv:1811.12152 (2018)"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Li, G., He, X., Zhang, W., Chang, H., Dong, L., Lin, L.: Non-locally enhanced encoder-decoder network for single image de-raining. arXiv preprint arXiv:1808.01491 (2018)","DOI":"10.1145\/3240508.3240636"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y., Tang, S., Ye, Y., Ma, J.: Spatial-aware non-local attention for fashion landmark detection. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 820\u2013825. IEEE (2019)","DOI":"10.1109\/ICME.2019.00146"},{"key":"29_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1007\/978-3-030-20876-9_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"X Liao","year":"2019","unstructured":"Liao, X., He, L., Yang, Z., Zhang, C.: Video-based person re-identification via 3D convolutional networks and non-local attention. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 620\u2013634. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20876-9_39"},{"key":"29_CR17","unstructured":"Lu, J., Sibai, H., Fabry, E.: Adversarial examples that fool detectors. CoRR abs\/1712.02494 (2017)"},{"key":"29_CR18","unstructured":"Lu, J., Sibai, H., Fabry, E., Forsyth, D.A.: No need to worry about adversarial examples in object detection in autonomous vehicles. CoRR abs\/1707.03501 (2017)"},{"key":"29_CR19","unstructured":"Lu, N., Yu, W., Qi, X., Chen, Y., Gong, P., Xiao, R.: MASTER: multi-aspect non-local network for scene text recognition. arXiv preprint arXiv:1910.02562 (2019)"},{"key":"29_CR20","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 4898\u20134906 (2016)"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, X., Bai, S., Wang, L., He, D., Liu, A.: Coarse-to-fine image inpainting via region-wise convolutions and non-local correlation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3123\u20133129. AAAI Press (2019)","DOI":"10.24963\/ijcai.2019\/433"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372\u2013387 (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Non-local graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv:1805.07694 (2018)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"29_CR25","unstructured":"Shokri, M., Harati, A., Taba, K.: Salient object detection in video using deep non-local neural networks. arXiv preprint arXiv:1810.07097 (2018)"},{"key":"29_CR26","unstructured":"Song, D., et al.: Physical adversarial examples for object detectors. In: 12th USENIX Workshop on Offensive Technologies (WOOT 2018) (2018)"},{"key":"29_CR27","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (2014). http:\/\/arxiv.org\/abs\/1312.6199"},{"key":"29_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-3-030-11018-5_20","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"Y Tang","year":"2019","unstructured":"Tang, Y., Zhang, X., Wang, J., Chen, S., Ma, L., Jiang, Y.-G.: Non-local NetVLAD encoding for video classification. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11132, pp. 219\u2013228. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11018-5_20"},{"key":"29_CR29","unstructured":"Tu, Z., Ma, Y., Li, C., Tang, J., Luo, B.: Edge-guided non-local fully convolutional network for salient object detection. arXiv preprint arXiv:1908.02460 (2019)"},{"key":"29_CR30","doi-asserted-by":"publisher","first-page":"7313","DOI":"10.1109\/ACCESS.2020.2964043","volume":"8","author":"S Wang","year":"2020","unstructured":"Wang, S., Hou, X., Zhao, X.: Automatic building extraction from high-resolution aerial imagery via fully convolutional encoder-decoder network with non-local block. IEEE Access 8, 7313\u20137322 (2020)","journal-title":"IEEE Access"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"29_CR32","unstructured":"Wang, Y., Seo, J., Jeon, T.: NL-LinkNet: toward lighter but more accurate road extraction with non-local operations. arXiv preprint arXiv:1908.08223 (2019)"},{"key":"29_CR33","unstructured":"Xia, B.N., Gong, Y., Zhang, Y., Poellabauer, C.: Second-order non-local attention networks for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3760\u20133769 (2019)"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.L.: Adversarial examples for semantic segmentation and object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1378\u20131387 (2017)","DOI":"10.1109\/ICCV.2017.153"},{"key":"29_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1007\/978-3-030-11018-5_46","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Xu","year":"2019","unstructured":"Xu, X., Wang, J.: Extended non-local feature for visual saliency detection in low contrast images. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11132, pp. 580\u2013592. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11018-5_46"},{"key":"29_CR36","unstructured":"Yue, K., Sun, M., Yuan, Y., Zhou, F., Ding, E., Xu, F.: Compact generalized non-local network. In: Advances in Neural Information Processing Systems, pp. 6510\u20136519 (2018)"},{"key":"29_CR37","doi-asserted-by":"crossref","unstructured":"Zajac, M., Zo\u0142na, K., Rostamzadeh, N., Pinheiro, P.O.: Adversarial framing for image and video classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 10077\u201310078 (2019)","DOI":"10.1609\/aaai.v33i01.330110077"},{"key":"29_CR38","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)"},{"key":"29_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: Asymmetric non-local neural networks for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 593\u2013602 (2019)","DOI":"10.1109\/ICCV.2019.00068"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58565-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:09:27Z","timestamp":1731283767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58565-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585648","9783030585655"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58565-5_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"3","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}