%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 (maxime\056portaz\100gmail\056com\073 matthias\056kohl\100etu\056univ\055grenoble\055alpes\056fr\073 jean\055pierre\056chevallet\100imag\056fr\073 georges\056quenot\100imag\056fr) /Producer (PyPDF2) /Author (This paper presents a novel approach for egocentric image retrieval and object detection\056 This approach uses fully convolutional network \050FCN\051 to obtain region proposals without the need for an additional component in the network and training\056 It is particularly suited for small dataset with low object variability\056 The proposed network can be trained end\055to\055end and it produces an effective global descriptor as image representation\056 Additionally\054 it can be built upon any type of CNN pre\055trained for classification\056 Through multiple experiments on two egocentric images datasets taken from museum visits\054 we show that the descriptor obtained using our proposed network outperforms those from previous state\055of\055the\055art approaches\056 It is also just as memoryefficient\054 making it adapted to mobile device like augmented museum audio\055guide\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 /x6 17 0 R /x12 20 0 R /x10 23 0 R >> /ExtGState << /s9 26 0 R /R11 29 0 R /s11 30 0 R /a0 << /CA 1 /ca 1 >> /s5 33 0 R /s7 36 0 R >> /Font << /F2 39 0 R /R12 40 0 R /F1 44 0 R /R14 45 0 R /R16 49 0 R /R18 52 0 R >> /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] >> /MediaBox [ 0 0 612 792 ] /Annots [ 56 0 R 57 0 R 58 0 R ] >> endobj 4 0 obj << /Parent 1 0 R /Rotate 0 /Type /Page /Contents 59 0 R /Resources << /ExtGState << /R11 29 0 R >> /Font << /F2 60 0 R /R12 40 0 R /F1 61 0 R /R14 45 0 R /R70 62 0 R >> /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] >> /MediaBox [ 0 0 612 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