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diff --git a/scraper/samples/s2-orc-paper.json b/scraper/samples/s2-orc-paper.json new file mode 100644 index 00000000..1fb29126 --- /dev/null +++ b/scraper/samples/s2-orc-paper.json @@ -0,0 +1,131 @@ +{ + "entities": [ + "Database", + "Experiment", + "Facial recognition system", + "Optimization problem", + "Program optimization", + "Simultaneous localization and mapping", + "Taxicab geometry", + "The Matrix", + "Video game localization" + ], + "journalVolume": "", + "journalPages": "3871-3879", + "pmid": "", + "year": 2015, + "outCitations": [ + "bd5accc96a772f2bbb7c64af0aa0b600b0b8594b", + "0e3fcfe63b7b6620e3c47e9751fe3456e85cc52f", + "023f6fc69fe1f6498e35dbf85932ecb549d36ca4", + "54aafe33c4a32fb5875d04fd4a6e6da50920c263", + "9f87e3212ab1d89c5d46924c925d6bb1da02f92b", + "0f0fcf041559703998abf310e56f8a2f90ee6f21", + "53b919c994b3658cca8178e455681ca244a7afad", + "084bd02d171e36458f108f07265386f22b34a1ae", + "14ce7635ff18318e7094417d0f92acbec6669f1c", + "e42998bbebddeeb4b2bedf5da23fa5c4efc976fa", + "0cb48f543c4bf329a16c0408c4d2c198679a6057", + "0de91641f37b0a81a892e4c914b46d05d33fd36e", + "3f204a413d9c8c16f146c306c8d96b91839fed0c", + "3393459600368be2c4c9878a3f65a57dcc0c2cfa", + "1824b1ccace464ba275ccc86619feaa89018c0ad", + "70eeacf9f86ba08fceb3dd703cf015016dac1930", + "5040f7f261872a30eec88788f98326395a44db03", + "95f12d27c3b4914e0668a268360948bce92f7db3", + "2ee56d6c29072abd576ef16bcb26360c42caaf5f", + "370b5757a5379b15e30d619e4d3fb9e8e13f3256", + "0296fc4d042ca8657a7d9dd02df7eb7c0a0017ad", + "129388116bc3229546a84b9bc3d11fdac8b93201", + "63f9f3f0e1daede934d6dde1a84fb7994f8929f0", + "03f98c175b4230960ac347b1100fbfc10c100d0c", + "32d6ec2810b52d1df128be51464bc43b53702232", + "1a1a60fd4dc88a14c016b95789385801c6b80574", + "800683c891b9c5934246ce2931d1d28e0a364fbf", + "0e986f51fe45b00633de9fd0c94d082d2be51406", + "0dccc881cb9b474186a01fd60eb3a3e061fa6546", + "f731b6745d829241941307c3ebf163e90e200318", + "07119bc66e256f88b7436e62a4ac3384365e4e9b", + "0a5bf19c1b297713a3267bc670b252f9e51c2b78", + "013909077ad843eb6df7a3e8e290cfd5575999d2", + "177bc509dd0c7b8d388bb47403f28d6228c14b5c", + "b2d9877443ec7da2490027ccc932468f05c7bf85", + "b960984881665f692764069015764d25974c8d1b", + "4998462014c907c519ae801af39cf58a4c538bc9", + "5d1c4e93e32ee686234c5aae7f38025523993c8c", + "64d5772f44efe32eb24c9968a3085bc0786bfca7", + "1c08824da1383051fba69384a3edf135ba58e7fd", + "b5a3cc76ceee9489e66c5929c66aec1cf5210241", + "4068574b8678a117d9a434360e9c12fe6232dae0", + "830e5b1043227fe189b3f93619ef4c58868758a7", + "044d9a8c61383312cdafbcc44b9d00d650b21c70", + "3957b51b44f8727fe008162ea8a142a8c7917dea", + "55b4b1168c734eeb42882082bd131206dbfedd5b", + "140438a77a771a8fb656b39a78ff488066eb6b50", + "31a38fd2d9d4f34d2b54318021209fe5565b8f7f" + ], + "s2Url": "https://semanticscholar.org/paper/788a7b59ea72e23ef4f86dc9abb4450efefeca41", + "s2PdfUrl": "", + "id": "788a7b59ea72e23ef4f86dc9abb4450efefeca41", + "authors": [ + { + "name": "Christos Sagonas", + "ids": [ + "3320415" + ] + }, + { + "name": "Yannis Panagakis", + "ids": [ + "1780393" + ] + }, + { + "name": "Stefanos Zafeiriou", + "ids": [ + "1776444" + ] + }, + { + "name": "Maja Pantic", + "ids": [ + "1694605" + ] + } + ], + "journalName": "2015 IEEE International Conference on Computer Vision (ICCV)", + "paperAbstract": "Recently, it has been shown that excellent resultscan be achieved in both facial landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D facial data. In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only. By observing that the frontal facial image is the one having the minimum rank of all different poses, an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix l1 norm is solved. The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions. The relevant experiments have been conducted on 8 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.", + "inCitations": [ + "0e2365254f7833a1a902a6439f42ece6e5326332", + "368d59cf1733af511ed8abbcbeb4fb47afd4da1c", + "0db8e6eb861ed9a70305c1839eaef34f2c85bbaf", + "fd46586ff7d1cdedc184051268bb07a6630bc72e", + "239401dc0dc4c64aa741a75f7324768ef7557889", + "ef21738c40f78fd5834cf5910fc58f45f88cf698", + "566c104c9a494ae385ab253e3c69864f69d53b52", + "5cf7ac83f2b63f2c090757bcf08df4bd2dd63b81", + "c62c910264658709e9bf0e769e011e7944c45c90", + "b730f01e47151ccfaf568285acf21374efc53f88", + "0822cf1f041b2e572a038667474e79acab264ebf", + "566a2ede36a6493010ea42a7df49916739e00c9d", + "b1a3b19700b8738b4510eecf78a35ff38406df22", + "6e9a8a34ab5b7cdc12ea52d94e3462225af2c32c", + "a06b6d30e2b31dc600f622ab15afe5e2929581a7", + "52dfa36d756a46cf11e6537463add3bdd9bb4011" + ], + "pdfUrls": [ + "http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf", + "http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.441", + "http://openaccess.thecvf.com/content_iccv_2015/papers/Sagonas_Robust_Statistical_Face_ICCV_2015_paper.pdf", + "http://ibug.doc.ic.ac.uk/media/uploads/documents/robust_frontalization.pdf", + "http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Sagonas_Robust_Statistical_Face_ICCV_2015_paper.pdf", + "https://ibug.doc.ic.ac.uk/media/uploads/documents/robust_frontalization.pdf" + ], + "title": "Robust Statistical Face Frontalization", + "doi": "10.1109/ICCV.2015.441", + "sources": [ + "DBLP" + ], + "doiUrl": "https://doi.org/10.1109/ICCV.2015.441", + "venue": "2015 IEEE International Conference on Computer Vision (ICCV)" +}
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