{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:28Z","timestamp":1750220908381,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":25,"publisher":"ACM Press","license":[{"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.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1145\/3354031.3354045","type":"proceedings-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T12:34:07Z","timestamp":1569587647000},"page":"102-106","source":"Crossref","is-referenced-by-count":0,"title":["Different Goal-driven CNNs Affect Performance of Visual Encoding Models based on Deep Learning"],"prefix":"10.1145","author":[{"given":"Ziya","family":"Yu","sequence":"first","affiliation":[{"name":"PLA Strategy Support Force Information Engineering, University Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering, University Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering, University Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Tong","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering, University Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering, University Zhengzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3354031.3354045-1","doi-asserted-by":"crossref","unstructured":"Kay, K.N. 2017. Principles for models of neural information processing. Neuroimage. (2017), S1053811917306638. DOI=https:\/\/doi.org\/10.1016\/j.neuroimage.2017.08.016.","DOI":"10.1016\/j.neuroimage.2017.08.016"},{"key":"key-10.1145\/3354031.3354045-2","doi-asserted-by":"crossref","unstructured":"Thomas, N., Kay, Kendrick N., Shinji, N. and Gallant, Jack L. 2011. Encoding and decoding in fMRI. Neuroimage. 56, 2 (2011), 400--410. DOI= https:\/\/doi.org\/10.1016\/j.neuroimage.2010.07.073.","DOI":"10.1016\/j.neuroimage.2010.07.073"},{"key":"key-10.1145\/3354031.3354045-3","unstructured":"Lecun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. Nature. 521, 7553 (2015), 436--444."},{"key":"key-10.1145\/3354031.3354045-4","doi-asserted-by":"crossref","unstructured":"SCHMIDHUBER, J., 2015. Deep learning in neural networks: An overview. Neural Networks 61, 85--117. DOI= https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003.","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"key-10.1145\/3354031.3354045-5","doi-asserted-by":"crossref","unstructured":"Krizhevsky, A., Sutskever, I. and Hinton, G.E. 2012. Image net classification with deep convolutional neural networks, International Conference on Neural Information Processing Systems (2012)., 84--90. DOI= https:\/\/doi.org\/10.1145\/3065386.","DOI":"10.1145\/3065386"},{"key":"key-10.1145\/3354031.3354045-6","unstructured":"Denton, E., Chintala, S., Szlam, A., and Fergus, R., 2015. Deep generative image models using a laplacian pyramid of adversarial networks. International Conference on Neural Information Processing Systems. (Montreal, Canada, December 07-12, 2015). ACM,1486-1494."},{"key":"key-10.1145\/3354031.3354045-7","unstructured":"Ba, J., Mnih, V., and Kavukcuoglu, K., 2014. Multiple object recognition with visual attention, Computer Science. (Apr. 2015)"},{"key":"key-10.1145\/3354031.3354045-8","doi-asserted-by":"crossref","unstructured":"Wang, X., Shrivastava, A., and Gupta, A., 2017. A-fast-rcnn: hard positive generation via adversary for object detection. (July. 2017). DOI= https:\/\/doi.org\/10.1109\/CVPR.2017.324.","DOI":"10.1109\/CVPR.2017.324"},{"key":"key-10.1145\/3354031.3354045-9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T., 2014. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 39, 4, 640--651. DOI= https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683.","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"key-10.1145\/3354031.3354045-10","doi-asserted-by":"crossref","unstructured":"Cl Ment, F., Camille, C., Laurent, N., and Yann, L., 2013. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis & Machine Intelligence 35, 8, 1915--1929. DOI= https:\/\/doi.org\/10.1109\/TPAMI.2012.231.","DOI":"10.1109\/TPAMI.2012.231"},{"key":"key-10.1145\/3354031.3354045-11","doi-asserted-by":"crossref","unstructured":"Spreng, R.N., Mar, R.A., and Kim, A.S.N., 2014. The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: A quantitative meta-analysis. Journal of cognitive neuroscience 21, 3, 489--510. DOI= https:\/\/doi.org\/10.1162\/jocn.2008.21029.","DOI":"10.1162\/jocn.2008.21029"},{"key":"key-10.1145\/3354031.3354045-12","unstructured":"Agrawal, P., Stansbury, D., Malik, J., and Gallant, J.L., 2014. Pixels to voxels: modeling visual representation in the human brain. arXiv preprint. arXiv:1407.5104."},{"key":"key-10.1145\/3354031.3354045-13","doi-asserted-by":"crossref","unstructured":"Umut, G.L. and Marcel A J, V.G., 2015. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience the Official Journal of the Society for Neuroscience 35, 27, 10005--10014. DOI= https:\/\/doi.org\/10.1523\/JNEUROSCI.5023-14.2015.","DOI":"10.1523\/JNEUROSCI.5023-14.2015"},{"key":"key-10.1145\/3354031.3354045-14","doi-asserted-by":"crossref","unstructured":"Umut Güçlü, and M. A. J. V. Gerven. Increasingly complex representations of natural movies across the dorsal stream are shared between subjects. NeuroImage 145.Pt B(2015):329--336. DOI=https:\/\/doi.org\/10.1016\/j.neuroimage.2015.12.036.","DOI":"10.1016\/j.neuroimage.2015.12.036"},{"key":"key-10.1145\/3354031.3354045-15","doi-asserted-by":"crossref","unstructured":"Wen, H., Shi, J., Chen, W., and Liu, Z., 2018. Deep residual network predicts cortical representation and organization of visual features for rapid categorization. Sci Rep 8, 1, 3752.","DOI":"10.1038\/s41598-018-22160-9"},{"key":"key-10.1145\/3354031.3354045-16","doi-asserted-by":"crossref","unstructured":"Yamins, D.L. and Dicarlo, J.J., 2016. Using goal-driven deep learning models to understand sensory cortex. Nature neuroscience 19, 3, 356.","DOI":"10.1038\/nn.4244"},{"key":"key-10.1145\/3354031.3354045-17","unstructured":"Chang, N., Pyles, J. A., Gupta, A., Tarr, M. J., and Aminoff, E. M.. (2018). Bold5000: a public fmri dataset of 5000 images."},{"key":"key-10.1145\/3354031.3354045-18","unstructured":"Simonyan, K., and Zisserman, A.. (2014). Very deep convolutional networks for large-scale image recognition. Computer Science."},{"key":"key-10.1145\/3354031.3354045-19","doi-asserted-by":"crossref","unstructured":"Needell, D. and Vershynin, R., 2009. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Foundations of Computational Mathematics 9, 3, 317--334. DOI= https:\/\/doi.org\/10.1007\/s10208-008-9031-3.","DOI":"10.1007\/s10208-008-9031-3"},{"key":"key-10.1145\/3354031.3354045-20","doi-asserted-by":"crossref","unstructured":"Needell, D. and Vershynin, R., 2010. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE Journal of Selected Topics in Signal Processing 4, 2, 310--316. DOI= https:\/\/doi.org\/10.1109\/JSTSP.2010.2042412.","DOI":"10.1109\/JSTSP.2010.2042412"},{"key":"key-10.1145\/3354031.3354045-21","doi-asserted-by":"crossref","unstructured":"D Samuel, S., Chen, S., and Geraint, R., 2011. The surface area of human V1 predicts the subjective experience of object size. Nature neuroscience 14, 1, 28--30.","DOI":"10.1038\/nn.2706"},{"key":"key-10.1145\/3354031.3354045-22","unstructured":"Lee, H., Ekanadham, C., and Ng, A.Y., 2007. Sparse deep belief net model for visual area V2. International Conference on Neural Information Processing Systems. (Vancouver, British Columbia, Canada, December 03 - 06, 2007). 873--880."},{"key":"key-10.1145\/3354031.3354045-23","unstructured":"Vann, S.D., 2009. What does the retrosplenial cortex do? Nature reviews neuroscience 10, 11 (Oct. 2009), 792--802."},{"key":"#cr-split#-key-10.1145\/3354031.3354045-24.1","unstructured":"Mégevand P, Groppe DM, Goldfinger MS, et al. (2014). Seeing scenes: topographic visual hallucinations evoked by direct electrical stimulation of the parahippocampal place area. Journal of Neuroscience. 34"},{"key":"#cr-split#-key-10.1145\/3354031.3354045-24.2","doi-asserted-by":"crossref","unstructured":"(16) (April 2014), 5399--5405. DOI= https:\/\/doi.org\/10.1523\/JNEUROSCI.5202-13.2014.","DOI":"10.1523\/JNEUROSCI.5202-13.2014"}],"event":{"number":"4","sponsor":["Graduate School of Library, Information, and Media Studies, University of Tsukuba, Japan","Sichuan University"],"acronym":"ICBIP '19","name":"the 2019 4th International Conference","start":{"date-parts":[[2019,8,13]]},"location":"Chengdu, China","end":{"date-parts":[[2019,8,15]]}},"container-title":["Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019)  - ICBIP '19"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3354031.3354045","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3354045&ftid=2085629&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:55Z","timestamp":1750203895000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3354031.3354045"}},"subtitle":[],"proceedings-subject":"Biomedical Signal and Image Processing (ICBIP 2019)","short-title":[],"issued":{"date-parts":[[2019]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1145\/3354031.3354045","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}