%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 (hyf\100iis\056u\055tokyo\056ac\056jp\073 cai\055mj\100iis\056u\055tokyo\056ac\056jp\073 kera\100iis\056u\055tokyo\056ac\056jp\073 yonetani\100iis\056u\055tokyo\056ac\056jp\073 khiguchi\100iis\056u\055tokyo\056ac\056jp\073 ysato\100iis\056u\055tokyo\056ac\056jp) /Producer (PyPDF2) /Author (This work aims to develop a computer\055vision technique for understanding objects jointly attended by a group of people during social interactions\056 As a key tool to discover such objects of joint attention\054 we rely on a collection of wearable eye\055tracking cameras that provide a first\055person video of interaction scenes and points\055of\055gaze data of interacting parties\056 Technically\054 we propose a hierarchical conditional random field\055based model that can 1\051 localize events of joint attention temporally and 2\051 segment objects of joint attention spatially\056 We show that by alternating these two procedures\054 objects of joint attention can be discovered reliably even from cluttered scenes and noisy points\055of\055gaze data\056 Experimental results demonstrate that our approach outperforms several state\055of\055the\055art methods for co\055segmentation and joint attention discovery\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 /R32 17 0 R /x6 18 0 R /R28 21 0 R /R27 24 0 R /x12 25 0 R /R33 28 0 R /x10 29 0 R /R26 32 0 R >> /ExtGState << /s9 33 0 R /s11 36 0 R /R17 39 0 R /a0 << /CA 1 /ca 1 >> /s5 40 0 R /s7 43 0 R >> /Font << /F2 46 0 R /R29 47 0 R /F1 51 0 R /R18 52 0 R /R20 55 0 R /R22 59 0 R >> /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /ColorSpace << /R25 22 0 R >> >> /MediaBox [ 0 0 612 792 ] /Annots [ 63 0 R 64 0 R 65 0 R 66 0 R 67 0 R 68 0 R 69 0 R 70 0 R ] >> endobj 4 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