{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T11:37:50Z","timestamp":1725881870839},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319541891"},{"type":"electronic","value":"9783319541907"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-54190-7_28","type":"book-chapter","created":{"date-parts":[[2017,3,11]],"date-time":"2017-03-11T05:44:09Z","timestamp":1489211049000},"page":"456-471","source":"Crossref","is-referenced-by-count":0,"title":["ZigzagNet: Efficient Deep Learning for Real Object Recognition Based on 3D Models"],"prefix":"10.1007","author":[{"given":"Yida","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuzhuang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,12]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR abs\/1409.4842 (2014)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. CoRR abs\/1408.5093 (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"28_CR4","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: Alexnet-level accuracy with 50 $$\\times $$ fewer parameters and $$<{1}$$ \u00a0MB model size. CoRR abs\/1602.07360 (2016)"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR6","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105. Curran Associates Inc. (2012)"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR abs\/1602.07261 (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3D pose estimation. In: Proceedings of the IEEE CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298930"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Pepik, B., Benenson, R., Ritschel, T., Schiele, B.: What is holding back convnets for detection? CoRR abs\/1508.02844 (2015)","DOI":"10.1007\/978-3-319-24947-6_43"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: a benchmark for 3D object detection in the wild. In: IEEE WACV (2014)","DOI":"10.1109\/WACV.2014.6836101"},{"key":"28_CR12","unstructured":"Courbariaux, M., Bengio, Y.: Binarynet: training deep neural networks with weights and activations constrained to $$+1$$ or $$-1$$ . Clinical Orthopaedics and Related Research (2016)"},{"key":"28_CR13","unstructured":"Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: training deep neural networks with binary weights during propagations (2015)"},{"key":"28_CR14","unstructured":"Gysel, P., Motamedi, M., Ghiasi, S.: Hardware-oriented approximation of convolutional neural networks (2016)"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. In: The IEEE ICCV (2015)","DOI":"10.1109\/ICCV.2015.308"},{"key":"28_CR16","unstructured":"Vedantham, A.: Guides. Flickr. Overview. (2013)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Dean, T., Ruzon, M., Segal, M., Shlens, J., Vijayanarasimhan, S., Yagnik, J.: Fast, accurate detection of 100,000 object classes on a single machine. In: 2013 IEEE Conference on CVPR, pp. 1814\u20131821 (2013)","DOI":"10.1109\/CVPR.2013.237"},{"key":"28_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/978-3-642-37331-2_42","volume-title":"Computer Vision \u2013 ACCV 2012","author":"S Hinterstoisser","year":"2013","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548\u2013562. Springer, Heidelberg (2013). doi: 10.1007\/978-3-642-37331-2_42"},{"key":"28_CR19","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. CoRR abs\/1409.0575 (2014)"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Benhimane, S., Lepetit, V., Fua, P., Navab, N.: Simultaneous recognition and homography extraction of local patches with a simple linear classifier. In: Proceedings of the BMVC, pp. 10.1\u201310.10. BMVA Press (2008)","DOI":"10.5244\/C.22.10"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. CoRR abs\/1404.4661 (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Jolliffe, I.T.: Principal component analysis. Technometrics (2014)","DOI":"10.1002\/9781118445112.stat06472"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. CoRR abs\/1502.01852 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"28_CR24","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs\/1502.03167 (2015)"},{"key":"28_CR25","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: an information-rich 3D model repository. Technical report arXiv:1512.03012 [cs.GR], Stanford University \u2013 Princeton University \u2013 Toyota Technological Institute at Chicago (2015)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2016"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-54190-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T23:32:33Z","timestamp":1658791953000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-54190-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319541891","9783319541907"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-54190-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}