{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:12:38Z","timestamp":1743005558157,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031263477"},{"type":"electronic","value":"9783031263484"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26348-4_30","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T07:03:47Z","timestamp":1678259027000},"page":"512-526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Energy-Efficient Image Processing Using Binary Neural Networks with\u00a0Hadamard Transform"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8382-0474","authenticated-orcid":false,"given":"Jaeyoon","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3858-0779","authenticated-orcid":false,"given":"Sunggu","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"30_CR1","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, pp. 4619\u20134629 (2021)","DOI":"10.1109\/CVPRW53098.2021.00520"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"6","key":"30_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"30_CR4","series-title":"Lecture Notes in Computational Vision and Biomechanics","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-61316-1_2","volume-title":"Biologically Rationalized Computing Techniques For Image Processing Applications","author":"A Diana Andrushia","year":"2018","unstructured":"Diana Andrushia, A., Thangarjan, R.: Saliency-based image compression using Walsh\u2013Hadamard transform (WHT). In: Hemanth, J., Balas, V.E. (eds.) Biologically Rationalized Computing Techniques For Image Processing Applications. LNCVB, vol. 25, pp. 21\u201342. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-61316-1_2"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"GG, L.P., Domnic, S.: Walsh-Hadamard transform kernel-based feature vector for shot boundary detection. IEEE Trans. Image Process. 23(12), 5187\u20135197 (2014)","DOI":"10.1109\/TIP.2014.2362652"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Ghrare, S.E., Khobaiz, A.R.: Digital image compression using block truncation coding and Walsh Hadamard transform hybrid technique. In: 2014 International Conference on Computer, Communications, and Control Technology (I4CT), pp. 477\u2013480. IEEE (2014)","DOI":"10.1109\/I4CT.2014.6914230"},{"key":"30_CR7","unstructured":"Gueguen, L., Sergeev, A., Kadlec, B., Liu, R., Yosinski, J.: Faster neural networks straight from JPEG. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"30_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":"30_CR9","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"30_CR10","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Advances in Neural Information Processing Systems 29 (2016)"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ju, S., Lee, Y., Lee, S.: Convolutional neural networks with discrete cosine transform features. IEEE Trans. Comput. 71, 3389\u20133395 (2022)","DOI":"10.1109\/TC.2022.3150574"},{"key":"30_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"30_CR13","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical Report (2009)"},{"key":"30_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (2012)"},{"key":"30_CR15","unstructured":"Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"30_CR16","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, pp. 6936\u20136946. PMLR (2021)"},{"key":"30_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-030-58568-6_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Shen, Z., Savvides, M., Cheng, K.-T.: ReActNet: towards precise binary neural network with generalized activation functions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 143\u2013159. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_9"},{"key":"30_CR18","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., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.-T.: 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":"30_CR19","unstructured":"Martinez, B., Yang, J., Bulat, A., Tzimiropoulos, G.: Training binary neural networks with real-to-binary convolutions. arXiv preprint arXiv:2003.11535 (2020)"},{"issue":"12","key":"30_CR20","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/0031-3203(92)90121-X","volume":"25","author":"R Mehrotra","year":"1992","unstructured":"Mehrotra, R., Namuduri, K.R., Ranganathan, N.: Gabor filter-based edge detection. Pattern Recogn. 25(12), 1479\u20131494 (1992)","journal-title":"Pattern Recogn."},{"key":"30_CR21","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Pan, H., Badawi, D., Cetin, A.E.: Fast Walsh-Hadamard transform and smooth-thresholding based binary layers in deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4650\u20134659 (2021)","DOI":"10.1109\/CVPRW53098.2021.00523"},{"issue":"1","key":"30_CR23","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/PROC.1969.6869","volume":"57","author":"WK Pratt","year":"1969","unstructured":"Pratt, W.K., Kane, J., Andrews, H.C.: Hadamard transform image coding. Proc. IEEE 57(1), 58\u201368 (1969)","journal-title":"Proc. IEEE"},{"key":"30_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107281","volume":"105","author":"H Qin","year":"2020","unstructured":"Qin, H., Gong, R., Liu, X., Bai, X., Song, J., Sebe, N.: Binary neural networks: a survey. Pattern Recogn. 105, 107281 (2020)","journal-title":"Pattern Recogn."},{"key":"30_CR25","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":"30_CR26","doi-asserted-by":"publisher","unstructured":"Salomon, D.: Data compression: the complete reference. Springer Science & Business Media (2004). https:\/\/doi.org\/10.1007\/978-1-84628-603-2","DOI":"10.1007\/978-1-84628-603-2"},{"key":"30_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108707","volume":"129","author":"M Ulicny","year":"2022","unstructured":"Ulicny, M., Krylov, V.A., Dahyot, R.: Harmonic convolutional networks based on discrete cosine transform. Pattern Recogn. 129, 108707 (2022)","journal-title":"Pattern Recogn."},{"issue":"4","key":"30_CR28","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/4233.897063","volume":"4","author":"I Valova","year":"2000","unstructured":"Valova, I., Kosugi, Y.: Hadamard-based image decomposition and compression. IEEE Trans. Inf Technol. Biomed. 4(4), 306\u2013319 (2000)","journal-title":"IEEE Trans. Inf Technol. Biomed."},{"key":"30_CR29","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27 (2014)"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pan, J., Liu, X., Chen, H., Chen, D., Zhang, Z.: FracBNN: accurate and fpga-efficient binary neural networks with fractional activations. In: The 2021 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 171\u2013182 (2021)","DOI":"10.1145\/3431920.3439296"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Z., Lew, L.: PokeBNN: a binary pursuit of lightweight accuracy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12475\u201312485 (2022)","DOI":"10.1109\/CVPR52688.2022.01215"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26348-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T07:19:28Z","timestamp":1678259968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26348-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263477","9783031263484"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26348-4_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","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":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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":"CMT Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","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)"}}]}}