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61 - 80 of 103 search results for KaKaoTalk:po03 op |u:mi.eng.cam.ac.uk where 0 match all words and 103 match some words.
  1. Results that match 1 of 2 words

  2. main.dvi

    mi.eng.cam.ac.uk/~sjy/papers/youn07
    20 Feb 2018: The advantage of this approach is that the op-timal transformation parameters can be determinedfrom the auxiliary function in a single pass over thedata[63].
  3. Refining Architectures of Deep Convolutional Neural Networks Sukrit…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2016-CVPR-refining-CNN.pdf
    13 Mar 2018: Please see Fig 1 for an illustration ofthese operations. We do not consider the other plausible op-erations for architectural refinement of CNN; for instance,arbitrary connection patterns between two layers ... 2. We introduce a strategy that starts with
  4. Silhouette-based Object Phenotype Recognition using 3D Shape Priors…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2011-ICCV-Chen-priors.pdf
    13 Mar 2018: However, the back-projectionfrom 2D to 3D is usually multi-modal, and this results ina non-convex objective function with multiple local op-tima, which is usually difficult to solve.
  5. Noname manuscript No.(will be inserted by the editor) Using ...

    mi.eng.cam.ac.uk/~cipolla/publications/article/2014-IJCV-dense-AAM.pdf
    13 Mar 2018: Dense op-. tical flow is used to compute pairwise registration and.
  6. A Unifying Resolution-Independent Formulation for Early Vision∗ Fabio …

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2012-CVPR-Viola.pdf
    13 Mar 2018: Our implementation has been optimizedfor speed at an algorithmic level by use of second order op-timizers, and by limiting the number of polygon clippingsrequired, but has not been micro-optimized.
  7. Ghostscript wrapper for…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-CVPR-Kim-tensor.pdf
    13 Mar 2018: rize human action and gesture classes in videos. Traditional. approaches based on explicit motion estimation require op-.
  8. bmvc-99.dvi

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/1999-BMVC-Chesi.pdf
    13 Mar 2018: 2Gênu£¢£nZ»i{ki£ªn¢a£ªs£iiÓ]i]n¢«sx¢g¤K1 ¡¥£B¢ai«Åig¢]s£iªs¢£g Ói isÅ1{Z£i3ìs«¥Â£pi£¥NM%¤n]kg]iPO ¢ ¡¥s£<-¿! $. iÓ]i]n¢n¥«¥ÅspJKs  P I6¥««3¤Kªs£g Óª££g MV OP I V
  9. stenger_imavis06.dvi

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2008-IVC-Stenger.pdf
    13 Mar 2018: Figure 11 illustrates the op-eration of the classifiers at different levels of the tree.
  10. Multi-Sensory Face Biometric Fusion (for Personal Identification)…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-OTCBVS-Arandjelovic-fusion.pdf
    13 Mar 2018: The optimal. values were found to be2.3 and6.2 for visual data; the op-timal filter for thermal data was found to be alow-passfilterwith W2 = 2.8 (i.e.
  11. Utilisation de la cohérence globale entre silhouettes pour…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-ARIF-Hernandez.pdf
    13 Mar 2018: intervals de profondeur vides.Dans le cas de deux vues, les silhouettes correspondantesne seront pas cohérentes s’il existe au moins un rayon op-tique classé S par une des silhouettes
  12. Large scale labelled video data augmentation for semantic…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2017-ICCV-label-propagation.pdf
    13 Mar 2018: However, in contrast to image classification and somedeep learning lead problems of computer vision, semanticsegmentation (especially for autonomous driving) still op-erates on limited size datasets which do not exceed 5000labelled
  13. DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-CVPR-Shankar.pdf
    13 Mar 2018: ntr. op. y L. os. s. Inp. ut. Ima. ge. L1.
  14. gasic_acltslp.dvi

    mi.eng.cam.ac.uk/~sjy/papers/gayo11.pdf
    20 Feb 2018: actions. The policy op-timisation is performed in interaction with a simulated user which gives a reward to the systemat the end of every dialogue.
  15. SegNet: A Deep Convolutional Encoder-Decoder Architecture for…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2015-arxiv-SegNet.pdf
    13 Mar 2018: LeCun. Sceneparsing with multiscale feature learning, purity trees, and op-timal covers.
  16. Silhouette-based Object Phenotype Recognition using 3D Shape Priors…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2011-ICCV-Chen-priors.pdf
    13 Mar 2018: However, the back-projectionfrom 2D to 3D is usually multi-modal, and this results ina non-convex objective function with multiple local op-tima, which is usually difficult to solve.
  17. Noname manuscript No.(will be inserted by the editor) Using ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2014-IJCV-dense-AAM.pdf
    13 Mar 2018: Dense op-. tical flow is used to compute pairwise registration and.
  18. Contour-Based Learning for Object Detection Jamie ShottonDepartment…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-ICCV-Shotton-object-detection.pdf
    13 Mar 2018: Horses were investigatedbriefly in [7] but poor results were obtained. 3Note that ROC curves are not ideal for the task of detection, as op-posed to classification; see [1] for more
  19. Ghostscript wrapper for…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-CVPR-Kim-tensor.pdf
    13 Mar 2018: rize human action and gesture classes in videos. Traditional. approaches based on explicit motion estimation require op-.
  20. bmvc-99.dvi

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/1999-BMVC-Chesi.pdf
    13 Mar 2018: 2Gênu£¢£nZ»i{ki£ªn¢a£ªs£iiÓ]i]n¢«sx¢g¤K1 ¡¥£B¢ai«Åig¢]s£iªs¢£g Ói isÅ1{Z£i3ìs«¥Â£pi£¥NM%¤n]kg]iPO ¢ ¡¥s£<-¿! $. iÓ]i]n¢n¥«¥ÅspJKs  P I6¥««3¤Kªs£g Óª££g MV OP I V
  21. 13 Mar 2018: tDvxmKyz{ag/koamn|@p s p}l}op z{ygKa. mm m. dKm pjym gp}@z{pg s kl z{ygKa. ... 4"<4L86"< )&A$"%( OP SOH! "% $ &%& 6 2 /, 1 ,"%5="% w4"< 2 "< 9 4w" 2 wZJ> 4w"%H7L.

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