{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:20:32Z","timestamp":1742930432324,"version":"3.40.3"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030110178"},{"type":"electronic","value":"9783030110185"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-11018-5_34","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T05:50:50Z","timestamp":1548309050000},"page":"379-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Targeted Kernel Networks: Faster Convolutions with Attentive Regularization"],"prefix":"10.1007","author":[{"given":"Kashyap","family":"Chitta","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. CoRR abs\/1603.04467 (2016)","key":"34_CR1"},{"unstructured":"Almahairi, A., Ballas, N., Cooijmans, T., Zheng, Y., Larochelle, H., Courville, A.C.: Dynamic capacity networks. CoRR abs\/1511.07838 (2015)","key":"34_CR2"},{"unstructured":"Ba, J., Mnih, V., Kavukcuoglu, K.: Multiple object recognition with visual attention. CoRR abs\/1412.7755 (2014)","key":"34_CR3"},{"doi-asserted-by":"crossref","unstructured":"Cao, C., et al.: Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: ICCV (2015)","key":"34_CR4","DOI":"10.1109\/ICCV.2015.338"},{"doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: CVPR (2017)","key":"34_CR5","DOI":"10.1109\/CVPR.2017.667"},{"unstructured":"Chen, T., Goodfellow, I.J., Shlens, J.: Net2Net: accelerating learning via knowledge transfer. CoRR abs\/1511.05641 (2015)","key":"34_CR6"},{"unstructured":"Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. CoRR abs\/1504.04788 (2015)","key":"34_CR7"},{"unstructured":"Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. ArXiv e-prints (2017)","key":"34_CR8"},{"unstructured":"Chollet, F.: Keras (2015). https:\/\/github.com\/fchollet\/keras","key":"34_CR9"},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1162\/NECO_a_00312","volume":"24","author":"M Denil","year":"2012","unstructured":"Denil, M., Bazzani, L., Larochelle, H., de Freitas, N.: Learning where to attend with deep architectures for image tracking. Neural Comput. 24, 2151\u20132184 (2012)","journal-title":"Neural Comput."},{"unstructured":"Denton, E., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: Exploiting linear structure within convolutional networks for efficient evaluation. CoRR abs\/1404.0736 (2014)","key":"34_CR11"},{"unstructured":"Dieleman, S., Fauw, J.D., Kavukcuoglu, K.: Exploiting cyclic symmetry in convolutional neural networks. CoRR abs\/1602.02660 (2016)","key":"34_CR12"},{"doi-asserted-by":"crossref","unstructured":"Dong, X., Huang, J., Yang, Y., Yan, S.: More is less: a more complicated network with less inference complexity. CoRR abs\/1703.08651 (2017). http:\/\/arxiv.org\/abs\/1703.08651","key":"34_CR13","DOI":"10.1109\/CVPR.2017.205"},{"unstructured":"Ekman, P., Friesen, W., Hager, J.: Facs manual. In: A Human Face (2002). https:\/\/www.scirp.org\/(S(i43dyn45teexjx455qlt3d2q))\/reference\/ReferencesPapers.aspx?ReferenceID=1850657","key":"34_CR14"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627\u20131645 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Figurnov, M., Vetrov, D.P., Kohli, P.: PerforatedCNNs: acceleration through elimination of redundant convolutions. CoRR abs\/1504.08362 (2015). http:\/\/arxiv.org\/abs\/1504.08362","key":"34_CR16"},{"doi-asserted-by":"crossref","unstructured":"Fu, J., Zheng, H., Mei, T.: Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: CVPR (2017)","key":"34_CR17","DOI":"10.1109\/CVPR.2017.476"},{"unstructured":"Girdhar, R., Ramanan, D.: Attentional pooling for action recognition. In: NIPS (2017)","key":"34_CR18"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. CoRR abs\/1504.08083 (2015)","key":"34_CR19","DOI":"10.1109\/ICCV.2015.169"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR abs\/1311.2524 (2013)","key":"34_CR20","DOI":"10.1109\/CVPR.2014.81"},{"unstructured":"Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: a recurrent neural network for image generation. In: ICML (2015)","key":"34_CR21"},{"unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding. CoRR abs\/1510.00149 (2015)","key":"34_CR22"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)","key":"34_CR23","DOI":"10.1109\/CVPR.2016.90"},{"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)","key":"34_CR24","DOI":"10.1109\/ICCV.2015.123"},{"doi-asserted-by":"crossref","unstructured":"Hendricks, L.A., Venugopalan, S., Rohrbach, M., Mooney, R.J., Saenko, K., Darrell, T.: Deep compositional captioning: describing novel object categories without paired training data. CoRR abs\/1511.05284 (2015)","key":"34_CR25","DOI":"10.1109\/CVPR.2016.8"},{"unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. ArXiv e-prints (2015)","key":"34_CR26"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, July 2017","key":"34_CR27","DOI":"10.1109\/CVPR.2017.243"},{"unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs\/1502.03167 (2015)","key":"34_CR28"},{"unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. CoRR abs\/1506.02025 (2015)","key":"34_CR29"},{"doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. CoRR abs\/1405.3866 (2014)","key":"34_CR30","DOI":"10.5244\/C.28.88"},{"unstructured":"Kawaguchi, K., Kaelbling, L.P., Bengio, Y.: Generalization in deep learning. ArXiv e-prints (2017)","key":"34_CR31"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014)","key":"34_CR32"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)","key":"34_CR33"},{"unstructured":"Larochelle, H., Hinton, G.E.: Learning to combine foveal glimpses with a third-order Boltzmann machine. In: NIPS (2010)","key":"34_CR34"},{"doi-asserted-by":"crossref","unstructured":"Lebedev, V., Lempitsky, V.S.: Fast ConvNets using group-wise brain damage. CoRR abs\/1506.02515 (2015)","key":"34_CR35","DOI":"10.1109\/CVPR.2016.280"},{"key":"34_CR36","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. ArXiv e-prints (2014)","key":"34_CR37"},{"doi-asserted-by":"crossref","unstructured":"Li, W., Abtahi, F., Zhu, Z.: Action unit detection with region adaptation, multi-labeling learning and optimal temporal fusing. CoRR abs\/1704.03067 (2017)","key":"34_CR38","DOI":"10.1109\/CVPR.2017.716"},{"doi-asserted-by":"crossref","unstructured":"Li, W., Abtahi, F., Zhu, Z., Yin, L.: EAC-NET: a region-based deep enhancing and cropping approach for facial action unit detection. CoRR abs\/1702.02925 (2017)","key":"34_CR39","DOI":"10.1109\/FG.2017.136"},{"unstructured":"Liao, Z., Carneiro, G.: Competitive multi-scale convolution. CoRR abs\/1511.05635 (2015)","key":"34_CR40"},{"unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. CoRR abs\/1312.4400 (2013)","key":"34_CR41"},{"doi-asserted-by":"crossref","unstructured":"Lu, J., Xiong, C., Parikh, D., Socher, R.: Knowing when to look: adaptive attention via a visual sentinel for image captioning. In: CVPR (2017)","key":"34_CR42","DOI":"10.1109\/CVPR.2017.345"},{"doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Prkachin, K.M., Solomon, P.E., Matthews, I.: Painful data: the UNBC-McMaster shoulder pain expression archive database. In: Face and Gesture 2011 (2011)","key":"34_CR43","DOI":"10.1109\/FG.2011.5771462"},{"doi-asserted-by":"crossref","unstructured":"Nam, H., Ha, J.W., Kim, J.: Dual attention networks for multimodal reasoning and matching. In: CVPR (2017)","key":"34_CR44","DOI":"10.1109\/CVPR.2017.232"},{"unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)","key":"34_CR45"},{"doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. CoRR abs\/1603.05279 (2016)","key":"34_CR46","DOI":"10.1007\/978-3-319-46493-0_32"},{"doi-asserted-by":"crossref","unstructured":"Ren, M., Pokrovsky, A., Yang, B., Urtasun, R.: SBNet: sparse blocks network for fast inference. CoRR abs\/1801.02108 (2018). http:\/\/arxiv.org\/abs\/1801.02108","key":"34_CR47","DOI":"10.1109\/CVPR.2018.00908"},{"unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs\/1506.01497 (2015)","key":"34_CR48"},{"unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. CoRR abs\/1412.6550 (2014)","key":"34_CR49"},{"unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. ArXiv e-prints (2017)","key":"34_CR50"},{"key":"34_CR51","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1142\/S012906579100011X","volume":"2","author":"J Schmidhuber","year":"1991","unstructured":"Schmidhuber, J., Huber, R.: Learning to generate artificial fovea trajectories for target detection. Int. J. Neural Syst. 2, 125\u2013134 (1991)","journal-title":"Int. J. Neural Syst."},{"unstructured":"Seo, P.H., Lin, Z., Cohen, S., Shen, X., Han, B.: Hierarchical attention networks. CoRR abs\/1606.02393 (2016)","key":"34_CR52"},{"doi-asserted-by":"crossref","unstructured":"Shih, K.J., Singh, S., Hoiem, D.: Where to look: focus regions for visual question answering. CoRR abs\/1511.07394 (2015)","key":"34_CR53","DOI":"10.1109\/CVPR.2016.499"},{"unstructured":"Shyam, P., Gupta, S., Dukkipati, A.: Attentive recurrent comparators. In: ICML (2017)","key":"34_CR54"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)","key":"34_CR55"},{"doi-asserted-by":"crossref","unstructured":"Srinivas, S., Babu, R.V.: Data-free parameter pruning for deep neural networks. CoRR abs\/1507.06149 (2015)","key":"34_CR56","DOI":"10.5244\/C.29.31"},{"key":"34_CR57","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition Benchmark: a multi-class classification competition. In: IEEE International Joint Conference on Neural Networks (2011)","key":"34_CR58","DOI":"10.1109\/IJCNN.2011.6033395"},{"unstructured":"Stollenga, M.F., Masci, J., Gomez, F.J., Schmidhuber, J.: Deep networks with internal selective attention through feedback connections. CoRR abs\/1407.3068 (2014)","key":"34_CR59"},{"doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: CVPR (2014)","key":"34_CR60","DOI":"10.1109\/CVPR.2014.244"},{"unstructured":"Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: ICML (2013)","key":"34_CR61"},{"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)","key":"34_CR62","DOI":"10.1609\/aaai.v31i1.11231"},{"unstructured":"Tai, C., Xiao, T., Wang, X., E, W.: Convolutional neural networks with low-rank regularization. CoRR abs\/1511.06067 (2015)","key":"34_CR63"},{"doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR (2014)","key":"34_CR64","DOI":"10.1109\/CVPR.2014.220"},{"doi-asserted-by":"crossref","unstructured":"Wu, B., Iandola, F.N., Jin, P.H., Keutzer, K.: SqueezeDet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. CoRR abs\/1612.01051 (2016)","key":"34_CR65","DOI":"10.1109\/CVPRW.2017.60"},{"unstructured":"Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., Zhang, Z.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. CoRR abs\/1411.6447 (2014)","key":"34_CR66"},{"unstructured":"Xiong, C., Merity, S., Socher, R.: Dynamic memory networks for visual and textual question answering. CoRR abs\/1603.01417 (2016)","key":"34_CR67"},{"unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. CoRR abs\/1502.03044 (2015)","key":"34_CR68"},{"doi-asserted-by":"crossref","unstructured":"Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: CVPR (2016)","key":"34_CR69","DOI":"10.1109\/CVPR.2016.10"},{"unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. CoRR abs\/1612.03928 (2016)","key":"34_CR70"},{"doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. CoRR abs\/1605.07146 (2016)","key":"34_CR71","DOI":"10.5244\/C.30.87"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wu, Y.N., Zhu, S.: Interpretable convolutional neural networks. CoRR abs\/1710.00935 (2017)","key":"34_CR72","DOI":"10.1109\/CVPR.2018.00920"},{"doi-asserted-by":"crossref","unstructured":"Zhao, K., Chu, W.S., Zhang, H.: Deep region and multi-label learning for facial action unit detection. In: CVPR (2016)","key":"34_CR73","DOI":"10.1109\/CVPR.2016.369"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11018-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T01:22:34Z","timestamp":1674350554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11018-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110178","9783030110185"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11018-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}