{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:24:32Z","timestamp":1742948672057,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985487"},{"type":"electronic","value":"9789819985494"}],"license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"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\/978-981-99-8549-4_6","type":"book-chapter","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T18:01:32Z","timestamp":1703440892000},"page":"65-76","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Binarizing Super-Resolution Neural Network Without Batch Normalization"],"prefix":"10.1007","author":[{"given":"Xunchao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Chao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.L.A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British Machine Vision Conference (BMVC) (2012)","DOI":"10.5244\/C.26.135"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Chen, B., et al.: Arm: any-time super-resolution method. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part XIX, pp. 254\u2013270. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-19800-7","DOI":"10.1007\/978-3-031-19800-7"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhang, Z., Ouyang, X., Liu, Z., Shen, Z., Wang, Z.: \u201cbnn-bn=?\u201d: training binary neural networks without batch normalization. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), pp. 4619\u20134629 (2021)","DOI":"10.1109\/CVPRW53098.2021.00520"},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Gao, G., Li, W., Li, J., Wu, F., Lu, H., Yu, Y.: Feature distillation interaction weighting network for lightweight image super-resolution. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), vol. 36, pp. 661\u2013669 (2022)","DOI":"10.1609\/aaai.v36i1.19946"},{"key":"6_CR7","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 (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR8","unstructured":"Helwegen, K., Widdicombe, J., Geiger, L., Liu, Z., Cheng, K.T., Nusselder, R.: Latent weights do not exist: Rethinking binarized neural network optimization. In: Advances in Neural Information Processing Systems (NeurIPS) 32 (2019)"},{"key":"6_CR9","doi-asserted-by":"publisher","unstructured":"Hong, C., Baik, S., Kim, H., Nah, S., Lee, K.M.: Cadyq: content-aware dynamic quantization for image super-resolution. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part VII, pp. 367\u2013383. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_22","DOI":"10.1007\/978-3-031-20071-7_22"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Hong, C., Kim, H., Baik, S., Oh, J., Lee, K.M.: Daq: channel-wise distribution-aware quantization for deep image super-resolution networks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2675\u20132684 (2022)","DOI":"10.1109\/WACV51458.2022.00099"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, X., Wang, N., Xin, J., Li, K., Yang, X., Gao, X.: Training binary neural network without batch normalization for image super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 1700\u20131707 (2021)","DOI":"10.1609\/aaai.v35i2.16263"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/978-3-030-58595-2_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Li","year":"2020","unstructured":"Li, H., et al.: PAMS: quantized super-resolution via parameterized max scale. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 564\u2013580. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_34"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: Local means binary networks for image super-resolution. IEEE Trans. Neural Netw. Learn. Syst. (TNNLS) (2022)","DOI":"10.1109\/TNNLS.2022.3212827"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"6_CR20","first-page":"7474","volume":"33","author":"M Lin","year":"2020","unstructured":"Lin, M., et al.: Rotated binary neural network. Adv. Neural Inform. Process. Syst. (NeurIPS) 33, 7474\u20137485 (2020)","journal-title":"Adv. Neural Inform. Process. Syst. (NeurIPS)"},{"key":"6_CR21","unstructured":"Liu, Z., Shen, Z., Li, S., Helwegen, K., Huang, D., Cheng, K.T.: How do adam and training strategies help bnns optimization. In: International Conference on Machine Learning (ICML), pp. 6936\u20136946. PMLR (2021)"},{"key":"6_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/978-3-030-01267-0_44","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Liu","year":"2018","unstructured":"Liu, Z., et al.: Bi-Real Net: enhancing the performance of 1-Bit CNNs with improved representational capability and advanced training algorithm. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 747\u2013763. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01267-0_44"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xiong, H., Hu, Z., Ma, L.: Efficient super resolution using binarized neural network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2019)","DOI":"10.1109\/CVPRW.2019.00096"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Qin, H., et al.: Forward and backward information retention for accurate binary neural networks. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), pp. 2250\u20132259 (2020)","DOI":"10.1109\/CVPR42600.2020.00232"},{"key":"6_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525\u2013542. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"key":"6_CR27","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1920\u20131927 (2013)","DOI":"10.1109\/ICCV.2013.241"},{"key":"6_CR29","unstructured":"Tipping, M., Bishop, C.: Bayesian image super-resolution. In: Advances in Neural Information Processing Systems (NeurIPS) 15 (2002)"},{"key":"6_CR30","doi-asserted-by":"publisher","unstructured":"Tu, Z., Chen, X., Ren, P., Wang, Y.: Adabin: improving binary neural networks with adaptive binary sets. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part XI. pp. 379\u2013395. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20083-0_23","DOI":"10.1007\/978-3-031-20083-0_23"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Wang, P., He, X., Cheng, J.: Toward accurate binarized neural networks with sparsity for mobile application. IEEE Trans. Neural Netw. Learn. Syst. (TNNLS) (2022)","DOI":"10.1109\/TNNLS.2022.3173498"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Lu, J., Zhou, J.: Bidet: an efficient binarized object detector. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), pp. 2049\u20132058 (2020)","DOI":"10.1109\/CVPR42600.2020.00212"},{"key":"6_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-58548-8_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Xin","year":"2020","unstructured":"Xin, J., Wang, N., Jiang, X., Li, J., Huang, H., Gao, X.: Binarized neural network for single image super resolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 91\u2013107. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_6"},{"key":"6_CR34","doi-asserted-by":"publisher","unstructured":"Xu, S., et al.: Ida-det: an information discrepancy-aware distillation for 1-bit detectors. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part XI, pp. 346\u2013361. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20083-0_21","DOI":"10.1007\/978-3-031-20083-0_21"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhao, J., Lu, J., Zhang, B., Han, S., Doermann, D.: Layer-wise searching for 1-bit detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5682\u20135691 (2021)","DOI":"10.1109\/CVPR46437.2021.00563"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: Recu: reviving the dead weights in binary neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5198\u20135208 (2021)","DOI":"10.1109\/ICCV48922.2021.00515"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"6_CR38","doi-asserted-by":"publisher","unstructured":"Zhong, Y., et al.: Dynamic dual trainable bounds for ultra-low precision super-resolution networks. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part XVIII, pp. 1\u201318. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_1","DOI":"10.1007\/978-3-031-19797-0_1"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, Y., Yuille, A.L.: Single image super-resolution using deformable patches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2917\u20132924 (2014)","DOI":"10.1109\/CVPR.2014.373"}],"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\/978-981-99-8549-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T20:06:49Z","timestamp":1730923609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8549-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,25]]},"ISBN":["9789819985487","9789819985494"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8549-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,25]]},"assertion":[{"value":"25 December 2023","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":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}