{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T17:49:02Z","timestamp":1766598542815,"version":"3.37.3"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["JQ18017"],"award-info":[{"award-number":["JQ18017"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61721004"],"award-info":[{"award-number":["61721004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11263-020-01415-x","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T08:03:41Z","timestamp":1609920221000},"page":"1087-1105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AutoDet: Pyramid Network Architecture Search for Object Detection"],"prefix":"10.1007","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9305-7924","authenticated-orcid":false,"given":"Zhihang","family":"Li","sequence":"first","affiliation":[]},{"given":"Teng","family":"Xi","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jingtuo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ran","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"issue":"6","key":"1415_CR1","first-page":"33","volume":"29","author":"EH Adelson","year":"1984","unstructured":"Adelson, E. H., Anderson, C. H., Bergen, J. R., Burt, P. J., & Ogden, J. M. (1984). Pyramid methods in image processing. RCA Engineer, 29(6), 33\u201341.","journal-title":"RCA Engineer"},{"key":"1415_CR2","unstructured":"Baker, B., Gupta, O., Naik, N., & Raskar, R. (2017). Designing neural network architectures using reinforcement learning. In ICLR."},{"key":"1415_CR3","doi-asserted-by":"crossref","unstructured":"Bell, S., Lawrence Zitnick, C., Bala, K., & Girshick, R. (2016). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In CVPR.","DOI":"10.1109\/CVPR.2016.314"},{"key":"1415_CR4","unstructured":"Bender, G., Kindermans, P. J., Zoph, B., Vasudevan, V., & Le, Q. (2018). Understanding and simplifying one-shot architecture search. In ICML."},{"key":"1415_CR5","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-nms-improving object detection with one line of code. ICCV, 5561\u20135569.","DOI":"10.1109\/ICCV.2017.593"},{"key":"1415_CR6","unstructured":"Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2018). Smash: One-shot model architecture search through hypernetworks. In ICLR."},{"key":"1415_CR7","doi-asserted-by":"crossref","unstructured":"Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. CVPR, 6154\u20136162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1415_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016). A unified multi-scale deep convolutional neural network for fast object detection. In ECCV.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"1415_CR9","unstructured":"Cai, H., Yang, J., Zhang, W., Han, S., & Yu, Y. (2018). Path-level network transformation for efficient architecture search. ICML, 677\u2013686."},{"key":"1415_CR10","unstructured":"Chen, L. C., Collins, M., Zhu, Y., Papandreou, G., Zoph, B., Schroff, F., et al. (2018). Searching for efficient multi-scale architectures for dense image prediction. NIPS, 8713\u20138724."},{"key":"1415_CR11","unstructured":"Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., & Sun, J. (2019). Detnas: Backbone search for object detection. NIPS, 6638\u20136648."},{"key":"1415_CR12","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. S. (2016). Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. arXiv preprint arXiv:1611.05594."},{"key":"1415_CR13","doi-asserted-by":"crossref","unstructured":"Chopard, B., & Tomassini, M. (2018). Simulated annealing. In An introduction to metaheuristics for optimization (pp. 59\u201379). Springer.","DOI":"10.1007\/978-3-319-93073-2_4"},{"key":"1415_CR14","unstructured":"Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In NIPS."},{"key":"1415_CR15","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., et al. (2017). Deformable convolutional networks. CVPR, 764\u2013773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"1415_CR16","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In CVPR.","DOI":"10.1109\/CVPR.2005.177"},{"key":"1415_CR17","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection."},{"key":"1415_CR18","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 CVPR (pp. 248\u2013255).","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"8","key":"1415_CR19","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, S., & Perona, P. (2014). Fast feature pyramids for object detection. TPAMI, 36(8), 1532\u20131545.","journal-title":"TPAMI"},{"key":"1415_CR20","doi-asserted-by":"crossref","unstructured":"Dong, X., & Yang, Y. (2019). Searching for a robust neural architecture in four gpu hours. CVPR, 1761\u20131770.","DOI":"10.1109\/CVPR.2019.00186"},{"key":"1415_CR21","unstructured":"Elsken, T., Metzen, J. H., & Hutter, F. (2018). Efficient multi-objective neural architecture search via lamarckian evolution. In ICLR."},{"issue":"55","key":"1415_CR22","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural architecture search: A survey. JMLR, 20(55), 1\u201321.","journal-title":"JMLR"},{"issue":"1","key":"1415_CR23","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. IJCV, 111(1), 98\u2013136.","journal-title":"IJCV"},{"key":"1415_CR24","unstructured":"Fu, C. Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017). Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659."},{"key":"1415_CR25","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T. Y., & Le, Q. V. (2019). Nas-fpn: Learning scalable feature pyramid architecture for object detection. CVPR, 7036\u20137045.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"1415_CR26","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. ICCv, 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"1415_CR27","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.","DOI":"10.1109\/CVPR.2014.81"},{"key":"1415_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask r-cnn. In ICCV.","DOI":"10.1109\/ICCV.2017.322"},{"key":"1415_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1415_CR30","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., Le, Q. V., & Adam, H. (2019). Searching for mobilenetv3. In ICCV.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1415_CR31","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wu, X., & He, R. (2020). Tf-nas: Rethinking three search freedoms of latency-constrained differentiable neural architecture search. In ECCV.","DOI":"10.1007\/978-3-030-58555-6_8"},{"key":"1415_CR32","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017). Speed\/accuracy trade-offs for modern convolutional object detectors. In CVPR.","DOI":"10.1109\/CVPR.2017.351"},{"key":"1415_CR33","unstructured":"Jenatton, R., Archambeau, C., Gonz\u00e1lez, J., & Seeger, M. (2017). Bayesian optimization with tree-structured dependencies. ICML, 1655\u20131664."},{"key":"1415_CR34","unstructured":"Jiang, N., Krishnamurthy, A., Agarwal, A., Langford, J., & Schapire, R. E. (2017). Contextual decision processes with low bellman rank are pac-learnable. In ICML (pp. 1704\u20131713). JMLR. org."},{"key":"1415_CR35","doi-asserted-by":"crossref","unstructured":"Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., & Chen, Y. (2017). Ron: Reverse connection with objectness prior networks for object detection. CVPR, 5936\u20135944.","DOI":"10.1109\/CVPR.2017.557"},{"key":"1415_CR36","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., & Sun, F. (2016). Hypernet: Towards accurate region proposal generation and joint object detection. In CVPR.","DOI":"10.1109\/CVPR.2016.98"},{"key":"1415_CR37","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS."},{"key":"1415_CR38","unstructured":"Law, H., & Deng, J. (2019). Cornernet: Detecting objects as paired keypoints. In IJCV."},{"key":"1415_CR39","unstructured":"Li, Z., & Zhou, F. (2017). Fssd: Feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960."},{"key":"1415_CR40","doi-asserted-by":"crossref","unstructured":"Li, Z., Xi, T., Deng, J., Zhang, G., Wen, S., & He, R. (2020). Gp-nas: Gaussian process based neural architecture search. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01195"},{"key":"1415_CR41","doi-asserted-by":"crossref","unstructured":"Li, S., Yang, L., Huang, J., Hua, X. S., & Zhang, L. (2019). Dynamic anchor feature selection for single-shot object detection. ICCV, 6609\u20136618.","DOI":"10.1109\/ICCV.2019.00671"},{"key":"1415_CR42","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. CVPR, 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"1415_CR43","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2017). Focal loss for dense object detection. ICCV, 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"1415_CR44","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2018). Focal loss for dense object detection. In TPAMI."},{"key":"1415_CR45","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In ECCV.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1415_CR46","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In ECCV.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1415_CR47","unstructured":"Liu, C., Chen, L. C., Schroff, F., Adam, H., Hua, W., Yuille, A., & Fei-Fei, L. (2019). Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. arXiv preprint arXiv:1901.02985."},{"key":"1415_CR48","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., et al. (2018). Receptive field block net for accurate and fast object detection. ECCV, 385\u2013400.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"1415_CR49","unstructured":"Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietik\u00e4inen, M. (2019). Deep learning for generic object detection: A survey. In IJCV."},{"key":"1415_CR50","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In CVPR.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1415_CR51","unstructured":"Liu, H., Simonyan, K., & Yang, Y. (2018). Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055."},{"key":"1415_CR52","unstructured":"Liu, H., Simonyan, K., Vinyals, O., Fernando, C., & Kavukcuoglu, K. (2017). Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436."},{"key":"1415_CR53","doi-asserted-by":"crossref","unstructured":"Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L. J., et al. (2018). Progressive neural architecture search. ECCV, 19\u201334.","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"1415_CR54","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In CVPR.","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"1415_CR55","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. IJCV, 60(2), 91\u2013110.","journal-title":"IJCV"},{"key":"1415_CR56","unstructured":"Luo, R., Tian, F., Qin, T., Chen, E., & Liu, T. Y. (2018). Neural architecture optimization. NIPS, 7816\u20137827."},{"key":"1415_CR57","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., & Deng, J. (2016). Stacked hourglass networks for human pose estimation. In ECCV.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"1415_CR58","doi-asserted-by":"crossref","unstructured":"Pang, Y., Wang, T., Anwer, R. M., Khan, F. S., & Shao, L. (2019). Efficient featurized image pyramid network for single shot detector. CVPR, 7336\u20137344.","DOI":"10.1109\/CVPR.2019.00751"},{"key":"1415_CR59","doi-asserted-by":"crossref","unstructured":"Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., et al. (2018). Megdet: A large mini-batch object detector. CVPR, 6181\u20136189.","DOI":"10.1109\/CVPR.2018.00647"},{"key":"1415_CR60","unstructured":"Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., & Dean, J. (2018). Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268."},{"key":"1415_CR61","unstructured":"Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2018). Regularized evolution for image classifier architecture search. arXiv preprint arXiv:1802.01548."},{"key":"1415_CR62","doi-asserted-by":"crossref","unstructured":"Redmon, J., & Farhadi, A. (2017). Yolo9000: Better, faster, stronger. CVPR, 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"1415_CR63","unstructured":"Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767."},{"key":"1415_CR64","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS."},{"key":"1415_CR65","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In MICCAI.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1415_CR66","doi-asserted-by":"crossref","unstructured":"Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., & Xue, X. (2017). Dsod: Learning deeply supervised object detectors from scratch. In ICCV.","DOI":"10.1109\/ICCV.2017.212"},{"key":"1415_CR67","unstructured":"Shrivastava, A., Sukthankar, R., Malik, J., & Gupta, A. (2016). Beyond skip connections: Top-down modulation for object detection. arXiv preprint arXiv:1612.06851."},{"key":"1415_CR68","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"1415_CR69","doi-asserted-by":"crossref","unstructured":"Singh, B., & Davis, L. S. (2018). An analysis of scale invariance in object detection snip. CVPR, 3578\u20133587.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"1415_CR70","unstructured":"Singh, B., Najibi, M., & Davis, L. S. (2018). Sniper: Efficient multi-scale training. NIPS, 9333\u20139343."},{"key":"1415_CR71","unstructured":"Suganuma, M., Ozay, M., & Okatani, T. (2018). Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. ICML, 4778\u20134787."},{"key":"1415_CR72","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1415_CR73","unstructured":"Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 6105\u20136114."},{"key":"1415_CR74","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., & Le, Q. V. (2018). Mnasnet: Platform-aware neural architecture search for mobile. arXiv preprint arXiv:1807.11626."},{"key":"1415_CR75","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., V. & Le, Q. (2020). Efficientdet: Scalable and efficient object detection. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1415_CR76","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017). Residual attention network for image classification. arXiv preprint arXiv:1704.06904."},{"key":"1415_CR77","doi-asserted-by":"crossref","unstructured":"Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., et al. (2019). Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. CVPR, 10734\u201310742.","DOI":"10.1109\/CVPR.2019.01099"},{"key":"1415_CR78","doi-asserted-by":"crossref","unstructured":"Xie, L., & Yuille, A. (2017). Genetic cnn. ICCV, 1379\u20131388.","DOI":"10.1109\/ICCV.2017.154"},{"key":"1415_CR79","unstructured":"Xie, S., Zheng, H., Liu, C., & Lin, L. (2018). Snas: Stochastic neural architecture search."},{"key":"1415_CR80","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Qiao, S., Xie, C., Shen, W., Wang, B., & Yuille, A. L. (2018). Single-shot object detection with enriched semantics. CVPR, 5813\u20135821.","DOI":"10.1109\/CVPR.2018.00609"},{"key":"1415_CR81","unstructured":"Zhang, C., Ren, M., & Urtasun, R. (2019). Graph hypernetworks for neural architecture search. In ICLR."},{"key":"1415_CR82","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018). Single-shot refinement neural network for object detection. CVPR, 4203\u20134212.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"1415_CR83","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., & Ling, H. (2019). M2det: A single-shot object detector based on multi-level feature pyramid network. In AAAI.","DOI":"10.1609\/aaai.v33i01.33019259"},{"key":"1415_CR84","doi-asserted-by":"crossref","unstructured":"Zheng, X., Ji, R., Tang, L., Zhang, B., Liu, J., & Tian, Q. (2019). Multinomial distribution learning for effective neural architecture search. In ICCV.","DOI":"10.1109\/ICCV.2019.00139"},{"key":"1415_CR85","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Yan, J., Wu, W., Shao, J., & Liu, C. L. (2018). Practical block-wise neural network architecture generation. CVPR, 2423\u20132432.","DOI":"10.1109\/CVPR.2018.00257"},{"key":"1415_CR86","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhao, C., Wang, J., Zhao, X., Wu, Y., & Lu, H. (2017). Couplenet: Coupling global structure with local parts for object detection. ICCV, 4126\u20134134.","DOI":"10.1109\/ICCV.2017.444"},{"key":"1415_CR87","unstructured":"Zoph, B., & Le, Q. V. (2016). Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578."},{"key":"1415_CR88","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. CVPR, 8697\u20138710.","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01415-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-020-01415-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01415-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T18:52:35Z","timestamp":1670698355000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-020-01415-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":88,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1415"],"URL":"https:\/\/doi.org\/10.1007\/s11263-020-01415-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"type":"print","value":"0920-5691"},{"type":"electronic","value":"1573-1405"}],"subject":[],"published":{"date-parts":[[2021,1,6]]},"assertion":[{"value":"3 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}