{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:29:22Z","timestamp":1740122962165,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key Research and Development Program of China \u201cnetwork collaborative manufacturing and intelligent factory\u201d key special project","award":["2018YFB1700702"],"award-info":[{"award-number":["2018YFB1700702"]}]},{"name":"Key Research and Development Projects of Sichuan Province","award":["2022YFG0246, 22ZDZX0051, 2021YFS0021"],"award-info":[{"award-number":["2022YFG0246, 22ZDZX0051, 2021YFS0021"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18581-6","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T06:02:04Z","timestamp":1708149724000},"page":"76873-76889","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient feature transform module"],"prefix":"10.1007","volume":"83","author":[{"given":"Ju","family":"Li","sequence":"first","affiliation":[]},{"given":"Yang","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3656-9480","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Huiyang","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"key":"18581_CR1","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"18581_CR2","doi-asserted-by":"crossref","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp\u00a0770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"18581_CR3","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition"},{"key":"18581_CR4","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a03431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"18581_CR5","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"18581_CR6","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"18581_CR7","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"18581_CR8","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp\u00a04780\u20134789","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"18581_CR9","unstructured":"LeCun Y, Denker J, Solla S (1989) Optimal brain damage. Advances in neural information processing systems 2"},{"key":"18581_CR10","doi-asserted-by":"crossref","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE international conference on computer vision, pp 2736\u20132744","DOI":"10.1109\/ICCV.2017.298"},{"key":"18581_CR11","doi-asserted-by":"crossref","unstructured":"Luo J.-H, Wu J, Lin W (2017) ThiNet: a filter level pruning method for deep neural network compression. In: Proceedings of the IEEE international conference on computer vision, pp 5058\u20135066","DOI":"10.1109\/ICCV.2017.541"},{"key":"18581_CR12","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. In: European conference on computer vision. Springer, pp 525\u2013542","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"18581_CR13","unstructured":"Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks. Advances in neural information processing systems 29"},{"key":"18581_CR14","unstructured":"Chen W, Wilson J, Tyree S, Weinberger K, Chen Y (2015) Compressing neural networks with the hashing trick. In: International conference on machine learning. PMLR, pp 2285\u20132294"},{"key":"18581_CR15","doi-asserted-by":"crossref","unstructured":"Jacob B, Kligys S, Chen B, Zhu M, Tang M, Howard A, Adam H, Kalenichenko D (2018) Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2704\u20132713","DOI":"10.1109\/CVPR.2018.00286"},{"key":"18581_CR16","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Y, Xu C, Yang Z, Liu C, Shi B, Xu C, Xu C, Tian Q (2019) Data-free learning of student networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3514\u20133522","DOI":"10.1109\/ICCV.2019.00361"},{"key":"18581_CR17","unstructured":"Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31"},{"key":"18581_CR18","unstructured":"Hinton G, Vinyals O, Dean J, et al (2015) Distilling the knowledge in a neural network 2(7). arXiv:1503.02531"},{"key":"18581_CR19","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"18581_CR20","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) 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","DOI":"10.1109\/CVPR.2018.00716"},{"key":"18581_CR21","unstructured":"Howard A.G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"18581_CR22","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V et al (2019) Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"18581_CR23","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L.-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"18581_CR24","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J (2018) ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"18581_CR25","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"3","key":"18581_CR26","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1109\/TIP.2018.2872876","volume":"28","author":"Y Li","year":"2019","unstructured":"Li Y, Liu D, Li H, Li L, Li Z, Wu F (2019) Learning a convolutional neural network for image compact-resolution. IEEE Trans Image Process 28(3):1092\u20131107. https:\/\/doi.org\/10.1109\/TIP.2018.2872876","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"18581_CR27","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/LSP.2017.2782270","volume":"25","author":"K Isogawa","year":"2018","unstructured":"Isogawa K, Ida T, Shiodera T, Takeguchi T (2018) Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Process Lett 25(2):224\u2013228. https:\/\/doi.org\/10.1109\/LSP.2017.2782270","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"18581_CR28","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18581_CR29","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems 25"},{"key":"18581_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"18581_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"18581_CR32","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and\u00a1 0.5 mb model size. arXiv:1602.07360"},{"key":"18581_CR33","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1016\/j.neucom.2022.06.066","volume":"501","author":"H Zhong","year":"2022","unstructured":"Zhong H, Lv Y, Yuan R, Yang D (2022) Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network. Neurocomputing 501:765\u2013777","journal-title":"Neurocomputing"},{"key":"18581_CR34","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images"},{"key":"18581_CR35","doi-asserted-by":"crossref","unstructured":"Silva E.A, Panetta K, Agaian SS (2007) Quantifying image similarity using measure of enhancement by entropy. In: Mobile multimedia\/image processing for military and security applications 2007, vol 6579, pp 219\u2013230. SPIE","DOI":"10.1117\/12.720087"},{"issue":"3","key":"18581_CR36","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/S0165-1684(98)00124-8","volume":"70","author":"MP Eckert","year":"1998","unstructured":"Eckert MP, Bradley AP (1998) Perceptual quality metrics applied to still image compression. Signal Process 70(3):177\u2013200","journal-title":"Signal Process"},{"key":"18581_CR37","doi-asserted-by":"crossref","unstructured":"Haase D, Amthor M (2020) Rethinking depthwise separable convolutions: how intra-kernel correlations lead to improved MobileNets. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14600\u201314609","DOI":"10.1109\/CVPR42600.2020.01461"},{"key":"18581_CR38","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448\u2013456"},{"key":"18581_CR39","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18581_CR40","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-NET: efficient channel attention for deep convolutional neural networks, pp 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"18581_CR41","unstructured":"Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946"},{"key":"18581_CR42","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neucom.2022.01.010","volume":"483","author":"C Nie","year":"2018","unstructured":"Nie C, Wang H (2018) Tensor neural networks via circulant convolution. Neurocomputing 483:22\u201331","journal-title":"Neurocomputing"},{"key":"18581_CR43","doi-asserted-by":"crossref","unstructured":"Yu R, Li A, Chen C-F, Lai J-H, Morariu VI, Han X, Gao M, Lin C-Y, Davis LS (2018) NISP: pruning networks using neuron importance score propagation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9194\u20139203","DOI":"10.1109\/CVPR.2018.00958"},{"key":"18581_CR44","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang N (2018) Data-driven sparse structure selection for deep neural networks. In: Proceedings of the European conference on computer vision (ECCV), pp 304\u2013320","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"18581_CR45","unstructured":"Yu J, Yang L, Xu N, Yang J, Huang T (2019) Slimmable neural networks"},{"key":"18581_CR46","doi-asserted-by":"crossref","unstructured":"Wu B, Wan A, Yue X, Jin P, Zhao S, Golmant N, Gholaminejad A, Gonzalez J, Keutzer K (2018) Shift: a zero flop, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9127\u20139135","DOI":"10.1109\/CVPR.2018.00951"},{"key":"18581_CR47","doi-asserted-by":"crossref","unstructured":"Molchanov P, Mallya A, Tyree S, Frosio I, Kautz J (2019) Importance estimation for neural network pruning. In: ICLR, pp 11264\u201311272","DOI":"10.1109\/CVPR.2019.01152"},{"key":"18581_CR48","doi-asserted-by":"crossref","unstructured":"Luo J-H, Wu J (2020) Neural network pruning with residual-connections and limited-data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1458\u20131467","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"18581_CR49","doi-asserted-by":"crossref","unstructured":"Liu Z, Mu H, Zhang X, Guo Z, Yang X, Cheng K-T, Sun J (2019) Metapruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a03296\u20133305","DOI":"10.1109\/ICCV.2019.00339"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18581-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18581-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18581-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T08:25:01Z","timestamp":1725351901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18581-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,17]]},"references-count":49,"journal-issue":{"issue":"31","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18581"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18581-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,2,17]]},"assertion":[{"value":"20 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have declared that no confict of interest exists.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}