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Tracking Using Online Feature Selectionand a Local Generative Model…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-BMVC-Woodley.pdf13 Mar 2018: We perform the adapted online feature selection algorithm (see Alg. 1) on a numberof test sequences. ... We take a single test image, and create a test sequence by adding fixed size, randomlypositioned black squares to simulate occlusion. -
A DATA-DRIVEN SPOKEN LANGUAGE UNDERSTANDING SYSTEM Yulan He Steve ...
mi.eng.cam.ac.uk/~sjy/papers/heyo03b.pdf20 Feb 2018: class-based trigram 4.8 3.4. Table 1. Test results for the speech recognizer (%WER). ... Forthe NL test, the semantic parser used as input the reference tran-scriptions instead of the recognized output. -
Learning Discriminative Canonical Correlationsfor Object Recognition…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2006-ECCV-Kim-imagesets.pdf13 Mar 2018: We used 18randomly selected training/test combinations for reporting identification rates. Comparative Methods. ... 0.9. 1. Dimension. Iden. tific. atio. n ra. te. Effect of the dimension on the test set. -
JOINT MODELLING OF VOICING LABEL AND CONTINUOUS F0 FOR ...
mi.eng.cam.ac.uk/~sjy/papers/yuyo11a.pdf20 Feb 2018: For the test material 30 sentences from a tourist information en-quiry application were used. ... JVF IVF. Fig. 2. Comparison between CF-IVF and CF-JVF on a forced choicepreference test. -
The Linear Transformation of LF Glottal Waveforms for Voice ...
mi.eng.cam.ac.uk/~sjy/papers/poyo08b20 Feb 2018: Figure 6: Results of the ABX test. between each source and target speaker pair. ... perc. enta. ge. PSHM. JEAS. MM MF FM FF. PSHMJEAS. Figure 7: Results of the quality comparison test. -
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2005-MVA-…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2005-MVA-Conde.pdf13 Mar 2018: 418. 2. DATA ACQUISITION SETUP In order to develop the tests here exposed, a set of 3D facial data have been acquired. ... The processing time was shorter than ten seconds even for the worst situation met during test step. -
stenger_imavis06.dvi
mi.eng.cam.ac.uk/~cipolla/publications/article/2008-IVC-Stenger.pdf13 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. -
A New Look at Filtering Techniques for Illumination Invariance ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2006-AFGR-Arandjelovic-filtering.pdf13 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, -
91_20090306_170604
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2009-MVA-Mavaddat.pdf13 Mar 2018: The model can now be used to classify test imagepatches as text or non-text. ... The test patches were ex-tracted in the same manner as the training patches. -
Incremental Learning of Locally OrthogonalSubspaces for Set-based…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2006-BMCV-Kim-incremental.pdf13 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. -
ADAPTATION OF AN EXPRESSIVE SINGLE SPEAKER DEEP NEURAL NETWORKSPEECH…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2018-ICASSP-speaker-adaptation.pdf3 May 2018: Test subjectswere asked to assess the quality of the speech on a 1-5 scale. ... 10 test subjectswere used. Models A and B are compared and models A and C arecompared. -
DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-CVPR-Shankar.pdf13 Mar 2018: M}. For a test image xt, the task is to predictyt A, i.e. ... The vali-dation set and the test set contain 2104 and 2967 imagesrespectively. -
Robust Instance Recognition in Presence ofOcclusion and Clutter Ujwal …
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2014-ECCV-3D-recognition.pdf13 Mar 2018: We capture six test scenes with the same five objects. Eachtest scene has 400 500 frames containing multiple objects with different back-grounds/clutter and poses.Scenario 4: This scenario tests ... Recall. Pre. cis. ion. LineModSupp. SIterative(Edge). -
Learning Motion Categories using both Semantic and Structural…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-CVPR-Wongsf-learning.pdf13 Mar 2018: Quantitative test was done on unsegmented KTH datasetusing the classifiers learnt in the previous experiment. ... In test set-up, we used unsegmentedKTH data for incremental training (i.e. -
0000010020030040050060070080090100110120130140150160170180190200210220…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2017-BMVC-bayesian-SegNet.pdf13 Mar 2018: This is achieved by sampling the network with randomly droppedout units at test time. ... Table 3: Pascal VOC12 [9] test results evaluated from the online evaluation server. -
Exploiting Sentence and Context Representations in Deep Neural…
mi.eng.cam.ac.uk/~sjy/papers/rgms16.pdf20 Feb 2018: Table 2 shows the results on the test sets. Consequently, when evaluating on the DSTC2 test set, awindow of 4 (w4), performs slightly better than other window sizes and better than ... On the In-car testset, a context window of 4 outperforms all the -
Chapter 1 Achieving Illumination Invariance using Image Filters…
mi.eng.cam.ac.uk/~cipolla/publications/contributionToEditedBook/2007-FR-chapter1.pdf13 Mar 2018: 0. 0.1. 0.2. 0.3. 0.4. 0.5. Test index. Rel. ativ. e re. ... The tests are shown in the order of increasing raw data performance foreasier visualization. -
C:/SFWDoc/Academic/Publications/2005/BMVC_2005/FinalPaper/bmvc_05_sfwo…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-BMVC-Wongsf-realtime.pdf13 Mar 2018: cluttered background, and background with skin colour). The overallaccuracyon 1025 test cases is 89.7%. ... Thepercentage of test cases that cannot be mapped into any classis 20.3%. -
TPAMI-0554-0706-2 1..14
mi.eng.cam.ac.uk/~cipolla/publications/article/2007-PAMI-Kim.pdf13 Mar 2018: We used 18 randomlyselected training/test combinations of the sequences forreporting identification rates. ... The test recognition rates changed byless than 1 percent for all of the different trials of randompartitioning. -
Gesture Recognition Under Small Sample Size Tae-Kyun Kim1 and ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-ACCV-Kim.pdf13 Mar 2018: High dimensional inputspace and a small training set often cause over-fitting of classifiers to the training data and poorgeneralization to new test data. ... 3. Nevertheless, the twointersection sets of the train and test sets are stillplaced in the -
nips7.dvi
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2005-ANIPS-Williams-VIC-algorithm.pdf13 Mar 2018: This is restrictive in practicein that test data may contain distortions that take it outside the strict ambit of the trainingpositives. ... This can be computed in terms of likelihoods. (1)so then the test becomes (2)where. -
Latent Intention Dialogue Models Tsung-Hsien Wen 1 * Yishu ...
mi.eng.cam.ac.uk/~sjy/papers/wmby17.pdf20 Feb 2018: During test-ing, we greedily selected the most probable intention andapplied beam search with the beamwidth set to 10 when de-coding the response. ... The significance test is based on atwo-tailed student-t test, between NDM and LIDMs. -
DISTRIBUTED DIALOGUE POLICIES FOR MULTI-DOMAIN STATISTICAL…
mi.eng.cam.ac.uk/~sjy/papers/gkty15.pdf20 Feb 2018: Bold values are statis-tically significant compared to non-bold values in the same groupusing an unpaired t-test with p < 0.01. ... The difference between bold valuesand non-bold values is statistically significant using an unpaired t-test where p < 0.02. -
Modelling Uncertainty in Deep Learning for Camera Relocalization Alex …
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2016-ICRA-pose-uncertainty.pdf13 Mar 2018: This is achieved by sampling the network withrandomly dropped out connections at test time. ... At test time we perform inference byaveraging stochastic samples from the dropout network. -
Conditional Generation and Snapshot Learning inNeural Dialogue…
mi.eng.cam.ac.uk/~sjy/papers/wgmr16a.pdf20 Feb 2018: 2016a). Statisticalsignificance was computed using two-tailed Wilcoxon Signed-Rank Test ( p <0.05) to compare models w/and w/o snapshot learning. ... 0.540 0.559 0.459. Table 2: Average activation of gates on test set. -
Using Wizard-of-Oz simulations to bootstrap…
mi.eng.cam.ac.uk/~sjy/papers/wiyo03.pdf20 Feb 2018: Sections 3 and 4 detail a method for addressing these issues, and the procedure used to test the method, respectively. -
Tracking Using Online Feature Selectionand a Local Generative Model…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-BMVC-Woodley.pdf13 Mar 2018: We perform the adapted online feature selection algorithm (see Alg. 1) on a numberof test sequences. ... We take a single test image, and create a test sequence by adding fixed size, randomlypositioned black squares to simulate occlusion. -
Label Propagation in Video Sequences Vijay Badrinarayanan†, Fabio…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2010-CVPR-label-propagation.pdf13 Mar 2018: RESULTS AND DISCUSSIONSAccuracy test Fig. 6 reproduces the quantitative results ofthe tests on Seq 1, 2 & 3. ... The comparable test accuracy to training under ground truth provides support for trainingclassifiers using the proposed methods. -
acl2010.dvi
mi.eng.cam.ac.uk/~sjy/papers/gjkm10.pdf20 Feb 2018: functionsfrom Table 1.The intention was, not only to test which algo-rithm yields the best policy performance, but alsoto examine the speed of convergence to the opti-mal policy. -
The Effect of Cognitive Load on a Statistical Dialogue ...
mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf20 Feb 2018: The averaged results are givenin Table 2. We performed a Kruskal test, followedby pairwise comparisons for every scenario for eachanswer and all differences are statistically signifi-cant (p < 0.03) apart -
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B:…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2006-SMC-localisation.pdf13 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. -
Learning Discriminative Canonical Correlationsfor Object Recognition…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-ECCV-Kim-imagesets.pdf13 Mar 2018: We used 18randomly selected training/test combinations for reporting identification rates. Comparative Methods. ... 0.9. 1. Dimension. Iden. tific. atio. n ra. te. Effect of the dimension on the test set. -
Multiscale Categorical Object RecognitionUsing Contour Fragments…
mi.eng.cam.ac.uk/~cipolla/publications/article/2008-PAMI-contour-recognition.pdf13 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). -
A Benchmarking Environment for ReinforcementLearning Based Task…
mi.eng.cam.ac.uk/~sjy/papers/cbsm17.pdf20 Feb 2018: To test the capability of algorithms in differentenvironments, a set of tasks has been defined that spans a wide range of environments across a numberof dimensions:. ... The models areevaluated over 500 test dialogues and the results shown are averaged -
Face Recognition with Image Sets Using Manifold Density Divergence ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-CVPR-Arandjelovic-divergence.pdf13 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. -
williams2006POMDPsForSDSs-manuscript
mi.eng.cam.ac.uk/~sjy/papers/wiyo07.pdf20 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 -
Face Set Classification using Maximally Probable Mutual Modes Ognjen…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2006-ICPR-Arandjelovic-faceset.pdf13 Mar 2018: To establish baseline performance, we compared ourrecognition algorithm to:. • State-of-the-art commercial system FaceItr by Identix[8] (the best performing software in the recent FaceRecognition Vendor Test [10]),. • ... perform well if imaging -
1 SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for Scene…
mi.eng.cam.ac.uk/~cipolla/publications/article/2016-PAMI-SegNet.pdf13 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. -
Reward Shaping with Recurrent Neural Networks for Speeding upOn-Line…
mi.eng.cam.ac.uk/~sjy/papers/svgm15.pdf20 Feb 2018: Prediction re-sults are shown in Figure 2 on two test sets; testA:1K dialogues, balanced regarding objective labels,at 15% SER and testB: containing 12K dialoguescollected at SERs of -
Reward-Balancing for Statistical Spoken Dialogue Systems…
mi.eng.cam.ac.uk/~sjy/papers/ubcy17.pdf20 Feb 2018: All TSR differences are statis-tically significant (t-test, p < 0.05). 4,000 dialogues in 10 batches. -
williams2006aaai.dvi
mi.eng.cam.ac.uk/~sjy/papers/wiyo06b.pdf20 Feb 2018: The corpus was segmented into a “trainingsub-corpus” and a “test sub-corpus,” which are each com-posed of an equal number of dialogs, the same mix of worderror rates, and ... increase, the average reward per turn decreases as expected,and in -
Emotion Conversion using F0 Segment Selection Zeynep Inanoglu, Steve…
mi.eng.cam.ac.uk/~sjy/papers/inyo08.pdf20 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. -
Stochastic Language Generation in Dialogueusing Factored Language…
mi.eng.cam.ac.uk/~sjy/papers/mayo14.pdf20 Feb 2018: occur simultaneously in the training and test partitions. In contrast, in our evaluation. -
STENT et al.: DETECTING CHANGE FOR MULTI-VIEW SURFACE INSPECTION ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-BMVC-change-detection.pdf13 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. -
Understanding Real World Indoor Scenes With Synthetic Data Ankur ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2016-CVPR-3D-synthetic-data.pdf13 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. -
stenger_imavis06.dvi
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2008-IVC-Stenger.pdf13 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. -
A New Look at Filtering Techniques for Illumination Invariance ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-AFGR-Arandjelovic-filtering.pdf13 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, -
Incremental Learning of Locally OrthogonalSubspaces for Set-based…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-BMCV-Kim-incremental.pdf13 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. -
Learning to Track with Multiple Observers Björn StengerComputer…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2009-CVPR-hand-tracking.pdf13 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 -
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-ICCV-relocalisation.pdf13 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.
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