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  2. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B:…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2006-SMC-localisation.pdf
    13 Mar 2018: Using the later index,Test-C tests Sequence-I, Test-D tests Sequence-II. The correctratios of the coarse localization are shown in Fig. ... Fig. 9. Layout of the outdoor environment in a campus. Test-E tests Sequence-III.
  3. Multiscale Categorical Object RecognitionUsing Contour Fragments…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2008-PAMI-contour-recognition.pdf
    13 Mar 2018: 9. Adding more parts helps performance on the test data up to a. ... effect on the RP EER (up to 100N percent for N test images).
  4. Face Recognition with Image Sets Using Manifold Density Divergence ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-CVPR-Arandjelovic-divergence.pdf
    13 Mar 2018: These meth-ods have achieved very good accuracy on a small number ofcontrolled test sets. ... We therefore useDKL(p(0)||p(i)) as a “distance measure” between trainingand test sets.
  5. 20 Feb 2018: 5. Perceptual EvaluationA direct comparison between F0 conversion methods was facil-itated using a three-way preference test. ... The same test was then conducted using converted neutralutterances generated by our conversion system.
  6. williams2006POMDPsForSDSs-manuscript

    mi.eng.cam.ac.uk/~sjy/papers/wiyo07.pdf
    20 Feb 2018: Partially Observable Markov Decision Processes for Spoken Dialog Systems. Jason D. Williams1 Steve Young AT&T Labs – Research Cambridge University. Engineering Department. Abstract. In a spoken dialog system, determining which action a machine
  7. Continuously Learning Neural Dialogue Management

    mi.eng.cam.ac.uk/~sjy/papers/sgmr16.pdf
    20 Feb 2018: Table 1 shows the weighted F-1 scores computedon the test set for each label.
  8. ICSLPDataCollection-10

    mi.eng.cam.ac.uk/~sjy/papers/wiyo04b.pdf
    20 Feb 2018: Thanks to Karl Weilheimer and Matt Stuttle for their assistance with the tests and for helpful comments on transcription conventions.
  9. 1 SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for Scene…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2016-PAMI-SegNet.pdf
    13 Mar 2018: 2. Fig. 1. SegNet predictions on urban and highway scene test samples from the wild. ... 1). Some example test resultsproduced on randomly sampled road scene images from Googleare shown in Fig.
  10. STENT et al.: DETECTING CHANGE FOR MULTI-VIEW SURFACE INSPECTION ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-BMVC-change-detection.pdf
    13 Mar 2018: This requires a limited effort incoarsely labelling a small subset of the test data. ... 6.1 Quantitative EvaluationFig. 5 illustrates change detection performance over the two test datasets.
  11. A New Look at Filtering Techniques for Illumination Invariance ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-AFGR-Arandjelovic-filtering.pdf
    13 Mar 2018: State-of-the-art commercial system FaceIt by Identix[12] (the best performing software in the most recentFace Recognition Vendor Test [13]),. • ... KLD) [14]. In all tests, both training data for each person in the gallery,as well as test data,
  12. 20 Feb 2018: Dataset Train Dev Test #SlotsRestaurants 1612 506 1117 4. Tourist Information 1600 439 225 9Table 5: Number of dialogues in the dataset splits usedfor the Dialogue State Tracking experiments.
  13. 20 Feb 2018: NCE is thus a suitable metricfor evaluating the accuracy of probability estimates given a setof hypotheses, but it does not necessarily test the overall cor-rectness of the output.
  14. 20 Feb 2018: occur simultaneously in the training and test partitions. In contrast, in our evaluation.
  15. Incremental Learning of Locally OrthogonalSubspaces for Set-based…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-BMCV-Kim-incremental.pdf
    13 Mar 2018: Iden. tific. atio. n ra. te. Effect of the dimension on the test set. ... Anindependent illumination set with both training and test sets was exploited for the val-idation.
  16. stenger_imavis06.dvi

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2008-IVC-Stenger.pdf
    13 Mar 2018: The parameters for both methods are setby testing the classification performance on a test setof 5000 images. ... In a first approach,the edge and colour cost terms are computed for a numberof test images.
  17. Template.dvi

    mi.eng.cam.ac.uk/~ar527/chen_asru2017.pdf
    15 Jun 2018: Thisconsists of about 1M words of acoustic transcription. Eightmeetingswere excluded from the training set and used as the developmentand test sets. ... Confusion network decoding canbe ap-plied on the rescored lattices and additional 0.3-0.4% WER
  18. Understanding Real World Indoor Scenes With Synthetic Data Ankur ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2016-CVPR-3D-synthetic-data.pdf
    13 Mar 2018: We also used dropout at test time [13] but observed verysimilar performance gain without it. ... However, dropout at. test time [13] makes the network robust to out-of-domaindata.
  19. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-ICCV-relocalisation.pdf
    13 Mar 2018: At test time we also normalize the quaternion orienta-tion vector to unit length. ... This noveldataset provides data to train and test pose regression algo-rithms in a large scale outdoor urban setting.
  20. Learning to Track with Multiple Observers Björn StengerComputer…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2009-CVPR-hand-tracking.pdf
    13 Mar 2018: The running of tests consisting of all possible combina-tions of all trackers on all test sequences would take a pro-hibitive amount of time to complete. ... In order to test the validity of such a setup, weperformed tests using the complete tracking
  21. Boosted Manifold Principal Angles for Image Set-Based Recognition…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2007-PR-Kim.pdf
    13 Mar 2018: single other – we used 9 randomly selected training/test combinations, see Figure 7. ... places low demands on storage space. 17. Table 2Evaluation results:The mean recognition rate and its standard deviation across differenttraining/test illuminations

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