{"refrec":{"BRefID":355439,"RR":"<b>Pedersen, M.; Haurum, J.B.; Moeslund, T.B.; Nyegaard, M.</b> (2022). Re-identification of giant sunfish using keypoint matching. <i>Proceedings of the Northern Lights Deep Learning Workshop 3</i>: 1-9. <a href=\"https://dx.doi.org/10.7557/18.6234\" target=\"_blank\">https://dx.doi.org/10.7557/18.6234</a>","BEntID":353152,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":". <i>Proceedings of the Northern Lights Deep Learning Workshop 3</i>: 1-9. <a href=\"https://dx.doi.org/10.7557/18.6234\" target=\"_blank\">https://dx.doi.org/10.7557/18.6234</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Pedersen, M.; Haurum, J.B.; Moeslund, T.B.; Nyegaard, M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Pedersen, M. <i>et al.</i>","Englishabstract":"We present the first work where re-identification ofthe Giant Sunfish (Mola alexandrini) is automated using computer vision and deep learning. We propose a pipeline that scores an mAP of 60.34% on a full rank of the novel TinyMola dataset which includes 31 IDs and 91 images. 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