{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:50:45Z","timestamp":1777654245323,"version":"3.51.4"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585679","type":"print"},{"value":"9783030585686","type":"electronic"}],"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\/978-3-030-58568-6_9","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T14:04:57Z","timestamp":1605189897000},"page":"143-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":262,"title":["ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions"],"prefix":"10.1007","author":[{"given":"Zechun","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhiqiang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Marios","family":"Savvides","sequence":"additional","affiliation":[]},{"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"9_CR1","unstructured":"Alizadeh, M., Fern\u00e1ndez-Marqu\u00e9s, J., Lane, N.D., Gal, Y.: An empirical study of binary neural networks\u2019 optimisation (2018)"},{"key":"9_CR2","unstructured":"Bethge, J., Bartz, C., Yang, H., Chen, Y., Meinel, C.: Meliusnet: can binary neural networks achieve mobilenet-level accuracy? arXiv preprint arXiv:2001.05936 (2020)"},{"key":"9_CR3","unstructured":"Martinez, B., Yang, J., Bulat, A., Tzimiropoulos, G.: Training binary neural networks with real-to-binary convolutions. In: International Conference on Learning Representations (2020)"},{"key":"9_CR4","unstructured":"Bulat, A., Tzimiropoulos, G.: XNOR-NET++: improved binary neural networks. In: British Machine Vision Conference (2019)"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Cai, Z., He, X., Sun, J., Vasconcelos, N.: Deep learning with low precision by half-wave gaussian quantization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5918\u20135926 (2017)","DOI":"10.1109\/CVPR.2017.574"},{"key":"9_CR6","unstructured":"Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: Advances in Neural Information Processing Systems, pp. 742\u2013751 (2017)"},{"key":"9_CR7","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to + 1 or -1. arXiv preprint arXiv:1602.02830 (2016)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Ding, R., Chin, T.W., Liu, Z., Marculescu, D.: Regularizing activation distribution for training binarized deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11408\u201311417 (2019)","DOI":"10.1109\/CVPR.2019.01167"},{"key":"9_CR9","unstructured":"Ding, X., Zhou, X., Guo, Y., Han, J., Liu, J., et al.: Global sparse momentum SGD for pruning very deep neural networks. In: Advances in Neural Information Processing Systems, pp. 6379\u20136391 (2019)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Faraone, J., Fraser, N., Blott, M., Leong, P.H.: SYG: learning symmetric quantization for efficient deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4300\u20134309 (2018)","DOI":"10.1109\/CVPR.2018.00452"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Gu, J., et al.: Projection convolutional neural networks for 1-bit CNNs via discrete back propagation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8344\u20138351 (2019)","DOI":"10.1609\/aaai.v33i01.33018344"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"9_CR13","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, pp. 7531\u20137542 (2019)"},{"key":"9_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"9_CR15","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"9_CR16","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Li, B., Shan, Y., Hu, M., Wang, Y., Chen, Y., Yang, H.: Memristor-based approximated computation. In: Proceedings of the 2013 International Symposium on Low Power Electronics and Design, pp. 242\u2013247. IEEE Press (2013)","DOI":"10.1109\/ISLPED.2013.6629302"},{"key":"9_CR18","unstructured":"Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)"},{"key":"9_CR19","unstructured":"Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. In: Advances in Neural Information Processing Systems, pp. 345\u2013353 (2017)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Circulant binary convolutional networks: enhancing the performance of 1-bit DCNNs with circulant back propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2691\u20132699 (2019)","DOI":"10.1109\/CVPR.2019.00280"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, W., Wu, B., Yang, X., Liu, W., Cheng, K.T.: Bi-real net: binarizing deep network towards real-network performance. Int. J. Comput. Vis. 128, 1\u201318 (2018)","DOI":"10.1007\/s11263-019-01227-8"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Metapruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3296\u20133305 (2019)","DOI":"10.1109\/ICCV.2019.00339"},{"key":"9_CR23","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":"9_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"9_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"key":"9_CR26","unstructured":"Mishra, A., Nurvitadhi, E., Cook, J.J., Marr, D.: WRPN: wide reduced-precision networks. arXiv preprint arXiv:1709.01134 (2017)"},{"key":"9_CR27","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, vol. 2011, p. 5 (2011)"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Phan, H., Liu, Z., Huynh, D., Savvides, M., Cheng, K.T., Shen, Z.: Binarizing mobilenet via evolution-based searching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13420\u201313429 (2020)","DOI":"10.1109\/CVPR42600.2020.01343"},{"key":"9_CR29","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, pp. 2250\u20132259 (2020)","DOI":"10.1109\/CVPR42600.2020.00232"},{"key":"9_CR30","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":"9_CR31","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Shen, Z., He, Z., Xue, X.: Meal: multi-model ensemble via adversarial learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4886\u20134893 (2019)","DOI":"10.1609\/aaai.v33i01.33014886"},{"key":"9_CR34","unstructured":"Soudry, D., Hubara, I., Meir, R.: Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights. In: Advances in Neural Information Processing Systems, pp. 963\u2013971 (2014)"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295\u20132329 (2017)","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Tang, W., Hua, G., Wang, L.: How to train a compact binary neural network with high accuracy? In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10862"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lu, J., Tao, C., Zhou, J., Tian, Q.: Learning channel-wise interactions for binary convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00066"},{"key":"9_CR38","unstructured":"Xu, Z., Cheung, R.C.: Accurate and compact convolutional neural networks withtrained binarization. In: British Machine Vision Conference (2019)"},{"key":"9_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-030-01237-3_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"D Zhang","year":"2018","unstructured":"Zhang, D., Yang, J., Ye, D., Hua, G.: LQ-Nets: learned quantization for highly accurate and compact deep neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 373\u2013390. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_23"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pan, Y., Yao, T., Zhao, H., Mei, T.: dabnn: a super fast inference framework for binary neural networks on arm devices. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 2272\u20132275 (2019)","DOI":"10.1145\/3343031.3350534"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"9_CR42","unstructured":"Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: towards lossless CNNs with low-precision weights. arXiv preprint arXiv:1702.03044 (2017)"},{"key":"9_CR43","unstructured":"Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: Dorefa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)"},{"key":"9_CR44","unstructured":"Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. arXiv preprint arXiv:1612.01064 (2016)"},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, S., Dong, X., Su, H.: Binary ensemble neural network: more bits per network or more networks per bit? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4923\u20134932 (2019)","DOI":"10.1109\/CVPR.2019.00506"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.: Towards effective low-bitwidth convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7920\u20137928 (2018)","DOI":"10.1109\/CVPR.2018.00826"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.: Structured binary neural networks for accurate image classification and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 413\u2013422 (2019)","DOI":"10.1109\/CVPR.2019.00050"}],"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\/978-3-030-58568-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:18:21Z","timestamp":1731370701000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58568-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585679","9783030585686"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58568-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 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. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 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)"}}]}}