%PDF-1.3 1 0 obj << /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] /Type /Pages /Count 9 >> endobj 2 0 obj << /Title (gberta\100seas\056upenn\056edu\073 jshi\100seas\056upenn\056edu) /Producer (PyPDF2) /Author (We present a first\055person method for cooperative basketball intention prediction\072 we predict with whom the camera wearer will cooperate in the near future from unlabeled first\055person images\056 This is a challenging task that requires inferring the camera wearer\047s visual attention\054 and decoding the social cues of other players\056 Our key observation is that a first\055person view provides strong cues to infer the camera wearer\047s intentions\056 We exploit this observation via a new cross\055model EgoSupervision learning scheme that allows us to predict with whom the camera wearer will cooperate\054 without using manually labeled intention labels\056 Our cross\055model EgoSupervision operates by transforming the outputs of a pretrained pose\055estimation network\054 into pseudo ground truth labels\054 which are then used as a supervisory signal to train a new network for a cooperative intention task\056 We evaluate our method\054 and show that it achieves similar or even better accuracy than the fully supervised methods do\056) /Subject (2017 IEEE International Conference on Computer Vision) >> endobj 3 0 obj << /Parent 1 0 R /Rotate 0 /Type /Page /Contents 13 0 R /Resources << /XObject << /x8 14 0 R /R30 17 0 R /R25 20 0 R /x6 21 0 R /R32 24 0 R /R29 25 0 R /R28 26 0 R /R27 27 0 R /R34 28 0 R /R35 29 0 R /x12 30 0 R /R33 33 0 R /x10 34 0 R /R26 37 0 R >> /ExtGState << /s9 38 0 R /s11 41 0 R /R8 44 0 R /a0 << /CA 1 /ca 1 >> /s5 45 0 R /s7 48 0 R >> /Font << /F2 51 0 R /R11 52 0 R /R13 56 0 R /R15 59 0 R /R9 63 0 R /R21 66 0 R /R23 70 0 R /F1 74 0 R >> /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /ColorSpace << /R18 18 0 R /R20 75 0 R >> >> /MediaBox [ 0 0 612 792 ] /Annots [ 77 0 R ] >> endobj 4 0 obj << /Parent 1 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