{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T09:04:35Z","timestamp":1780563875416,"version":"3.54.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"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":["Mach. Intell. Res."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11633-023-1434-8","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T03:54:53Z","timestamp":1732766093000},"page":"1192-1200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Branch Convolution Quantization for Object Detection"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4741-3043","authenticated-orcid":false,"given":"Miao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2586-5505","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuiting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"1434_CR1","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.5555\/2969442.2969588","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems","author":"M Courbariaux","year":"2015","unstructured":"M. Courbariaux, Y. Bengio, J. P. David. BinaryConnect: Training deep neural networks with binary weights during propagations. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 3123\u20133131, 2015. DOI: https:\/\/doi.org\/10.5555\/2969442.2969588."},{"key":"1434_CR2","volume-title":"Bitwise neural networks","author":"M Kim","year":"2016","unstructured":"M. Kim, P. Smaragdis. Bitwise neural networks, [Online], Available: https:\/\/arxiv.org\/abs\/1601.06071, 2016."},{"key":"1434_CR3","doi-asserted-by":"publisher","first-page":"4114","DOI":"10.5555\/3157382.3157557","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"I Hubara","year":"2016","unstructured":"I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio. Binarized neural networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 4114\u20134122, 2016. DOI: https:\/\/doi.org\/10.5555\/3157382.3157557."},{"key":"1434_CR4","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Proceedings of the 14th European Conference on Computer Vision","author":"M Rastegari","year":"2016","unstructured":"M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi. XNOR-Net: ImageNet classification using binary convolutional neural networks. In Proceedings of the 14th European Conference on Computer Vision, Springer, Amsterdam, The Netherlands, pp. 525\u2013542, 2016. DOI: https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32."},{"key":"1434_CR5","volume-title":"DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"S C Zhou","year":"2016","unstructured":"S. C. Zhou, Y. X. Wu, Z. K. Ni, X. Y. Zhou, H. Wen, Y. H. Zou. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients, [Online], Available: https:\/\/arxiv.org\/abs\/1606.06160, 2016."},{"key":"1434_CR6","doi-asserted-by":"publisher","unstructured":"H. T. Qin, R. H. Gong, X. L. Liu, X. Bai, J. K. Song, N. Sebe. Binary neural networks: A survey. Pattern Recognition, vol. 105, Article number 107281, 2020. DOI: https:\/\/doi.org\/10.1016\/j.patcog.2020.107281.","DOI":"10.1016\/j.patcog.2020.107281"},{"key":"1434_CR7","doi-asserted-by":"publisher","first-page":"7300","DOI":"10.1109\/CVPR.2019.00748","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"J W Yang","year":"2019","unstructured":"J. W. Yang, X. Shen, J. Xing, X. M. Tian, H. Q. Li, B. Deng, J. Q. Huang, X. S. Hua. Quantization networks. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 7300\u20137308, 2019. DOI: https:\/\/doi.org\/10.1109\/CVPR.2019.00748."},{"key":"1434_CR8","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/CVPR.2019.00292","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"R D Li","year":"2019","unstructured":"R. D. Li, Y. Wang, F. Liang, H. W. Qin, J. J. Yan, R. Fan. Fully quantized network for object detection. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 2805\u20132814, 2019. DOI: https:\/\/doi.org\/10.1109\/CVPR.2019.00292."},{"key":"1434_CR9","doi-asserted-by":"publisher","first-page":"20828","DOI":"10.1109\/ACCESS.2021.3054879","volume":"9","author":"S Kim","year":"2021","unstructured":"S. Kim, H. Kim. Zero-centered fixed-point quantization with iterative retraining for deep convolutional neural network-based object detectors. IEEE Access, vol. 9, pp. 20828\u201320839, 2021. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2021.3054879.","journal-title":"IEEE Access"},{"key":"1434_CR10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/FPT.2018.00014","volume-title":"Proceedings of International Conference on Field-Programmable Technology","author":"H X Fan","year":"2018","unstructured":"H. X. Fan, S. L. Liu, M. Ferianc, H. C. Ng, Z. Q. Que, S. Liu, X. Y. Niu, W. Luk. A real-time object detection accelerator with compressed SSDLite on FPGA. In Proceedings of International Conference on Field-Programmable Technology, IEEE, Naha, Japan, pp. 14\u201321, 2018. DOI: https:\/\/doi.org\/10.1109\/FPT.2018.00014."},{"issue":"3","key":"1434_CR11","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1109\/JET-CAS.2020.3015753","volume":"10","author":"J I Guo","year":"2020","unstructured":"J. I. Guo, C. C. Tsai, J. L. Zeng, S. W. Peng, E. C. Chang. Hybrid fixed-point\/binary deep neural network design methodology for low-power object detection. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 10, no. 3, pp. 388\u2013400, 2020. DOI: https:\/\/doi.org\/10.1109\/JET-CAS.2020.3015753.","journal-title":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems"},{"issue":"8","key":"1434_CR12","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1109\/TVLSI.2019.2905242","volume":"27","author":"D T Nguyen","year":"2019","unstructured":"D. T. Nguyen, T. N. Nguyen, H. Kim, H. J. Lee. A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 8, pp. 1861\u20131873, 2019. DOI: https:\/\/doi.org\/10.1109\/TVLSI.2019.2905242.","journal-title":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems"},{"key":"1434_CR13","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"A J Zhou","year":"2017","unstructured":"A. J. Zhou, A. B. Yao, Y. W. Guo, L. Xu, Y. R. Chen. Incremental network quantization: Towards lossless CNNs with low-precision weights. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 2017."},{"key":"1434_CR14","volume-title":"n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization","author":"Y Sakuma","year":"2021","unstructured":"Y. Sakuma, H. Sumihiro, J. Nishikawa, T. Nakamura, R. Ikegaya. n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization, [Online], Available: https:\/\/arxiv.org\/abs\/2103.11704, 2021."},{"issue":"4","key":"1434_CR15","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1109\/JSTSP.2020.3005030","volume":"14","author":"F Cardinaux","year":"2020","unstructured":"F. Cardinaux, S. Uhlich, K. Yoshiyama, J. A. Garc\u00eda, L. Mauch, S. Tiedemann, T. Kemp, A. Nakamura. Iteratively training look-up tables for network quantization. IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 860\u2013870, 2020. DOI: https:\/\/doi.org\/10.1109\/JSTSP.2020.3005030.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"1434_CR16","volume-title":"Post-training piecewise linear quantization for deep neural networks","author":"J Fang","year":"2020","unstructured":"J. Fang, A. Shafiee, H. Abdel-Aziz, D. Thorsley, G. Georgiadis, J. Hassoun. Post-training piecewise linear quantization for deep neural networks, [Online], Available: https:\/\/arxiv.org\/abs\/2002.00104, 2020."},{"key":"1434_CR17","doi-asserted-by":"publisher","first-page":"7197","DOI":"10.1109\/CVPR.2017.761","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"E Park","year":"2017","unstructured":"E. Park, J. Ahn, S. Yoo. Weighted-entropy-based quantization for deep neural networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 7197\u20137205, 2017. DOI: https:\/\/doi.org\/10.1109\/CVPR.2017.761."},{"key":"1434_CR18","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/FPT.2017.8280163","volume-title":"Proceedings of International Conference on Field Programmable Technology","author":"M Shimoda","year":"2017","unstructured":"M. Shimoda, S. Sato, H. Nakahara. All binarized convolutional neural network and its implementation on an FPGA. In Proceedings of International Conference on Field Programmable Technology, IEEE, Melbourne, Australia, pp. 291\u2013294, 2017. DOI: https:\/\/doi.org\/10.1109\/FPT.2017.8280163."},{"key":"1434_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/FPL.2018.00016","volume-title":"Proceedings of the 28th International Conference on Field Programmable Logic and Applications","author":"P Guo","year":"2018","unstructured":"P. Guo, H. Ma, R. Z. Chen, P. Li, S. L. Xie, D. L. Wang. FBNA: A fully binarized neural network accelerator. In Proceedings of the 28th International Conference on Field Programmable Logic and Applications, IEEE, Dublin, Ireland, pp. 51\u2013513, 2018. DOI: https:\/\/doi.org\/10.1109\/FPL.2018.00016."},{"key":"1434_CR20","doi-asserted-by":"publisher","first-page":"4918","DOI":"10.1109\/CVPR.2019.00506","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"S L Zhu","year":"2019","unstructured":"S. L. Zhu, X. Dong, H. Su. Binary ensemble neural network: More bits per network or more networks per bit? In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 4918\u20134927, 2019. DOI: https:\/\/doi.org\/10.1109\/CVPR.2019.00506."}],"container-title":["Machine Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-023-1434-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11633-023-1434-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-023-1434-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:33:11Z","timestamp":1780561991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11633-023-1434-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":20,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1434"],"URL":"https:\/\/doi.org\/10.1007\/s11633-023-1434-8","relation":{},"ISSN":["2731-538X","2731-5398"],"issn-type":[{"value":"2731-538X","type":"print"},{"value":"2731-5398","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"23 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declared that they have no conflicts of interest to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations of conflict of interest"}}]}}