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  2. Efficient Large-Scale Semantic VisualLocalization in 2D Maps Tomas…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2020-ACCV-Large-scale-localization.pdf
    28 Jun 2021: database of embeddings of the test environmentlocations to retrieve the most similar location. ... The plots in the middle show mean confidence of trajectories w.r.t.
  3. Contour-Based Learning for Object Detection Jamie ShottonDepartment…

    mi.eng.cam.ac.uk/reports/svr-ftp/shotton_iccv05.pdf
    8 Aug 2005: Test-ing was performed on 164 images containing 193 cars,and164 background images. ... Our technique relies on edge features andhigher-resolution training and test images would certainlyimprove results.
  4. 20 Feb 2018: The environment T in a training dia-logue might be different from that of the test dialogues, and thuswe computed a maximum-entropy randomised policy in the testenvironments given the learnt ... In future work, theIRL reward function will be integrated
  5. 20 Feb 2018: Table 3Percent of unseen contexts in the test data. Number of matching features. ... Noduration modification was applied for this test. Twenty subjects participated in the evaluation.
  6. paper.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/kim_icslp04.pdf
    10 Jan 2005: The transcription is either known in training orobtained by an initial, non-VTLN, decoding pass for test data.If and are the original and transformed feature vectorsrespectively then the log-likelihood ... The systems were eval-uated on two 3 hour test
  7. Towards Automatic Assessment of Spontaneous Spoken English Y.…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ALTA_SpComm2017.pdf
    12 Sep 2018: computed over all the test sectionswhere the candidate is required to produce spontaneousspeech. ... xN}, what is the best estimate of thevalue of the function at test point x.
  8. Ghostscript wrapper for…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-CVPR-Kim-incremental.pdf
    13 Mar 2018: test data. The descriptor should also be compact, even for. large data sets. ... divided into training and test sets. All basis vectors were. extracted from the training set.
  9. IMPLEMENTATION OF AUTOMATIC CAPITALISATIONGENERATION SYSTEMS FOR…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_icassp02.pdf
    9 Aug 2005: We also used 3 hours of test data from the NIST 1998 Hub-4BN benchmark tests. ... Most of these words are not capitalised,if they are used in the middle of sentences.
  10. Pronunciation modeling by sharing Gaussians

    mi.eng.cam.ac.uk/reports/svr-ftp/nock_csl00.pdf
    31 Jul 2000: Pronunciation modeling by sharing Gaussians 141. TABLE I. WER degradation with speaking style on theMULTI-REG test set. ... Steps (6), (3), (4) and (5) arethen carried out in that order to estimate and test a matched pronunciation model.
  11. X-MAN: Explaining multiple sources of anomalies in video Stanislaw ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2021-CVPR-XMAN-anomaly-detection.pdf
    9 Apr 2022: Bottom: Test frame detected as anomalous, showingat least one HOI vector has low probability under the GMM. ... Evaluation metric. All test video frames from alldatasets are marked as either containing or not containing ananomaly.
  12. More Robust Schema-Guided Dialogue State Tracking via…

    mi.eng.cam.ac.uk/~wjb31/eacl_2023_CR.pdf
    1 Mar 2023: Test set dialogues aregrounded in 6 schemas seen during training and 15unseen ones. ... 6It appears in 4 unseen services in the test set.7Lee et al.
  13. Optimisation of Fast LVCSR Systems Gunnar Evermann, Phil Woodland ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/evermann_stthomas03.pdf
    10 Dec 2003: P1 speed-accuracy trade-off (CTS eval02). • In eval chose middle operating point for safety Should have used fast setup and use time elsewhere. ... test on eval03: skip 66% segments, 43% audio, 32% rescoring runtimei.e.
  14. Learning to Track with Multiple Observers Björn StengerComputer…

    mi.eng.cam.ac.uk/~cipolla/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
  15. KIM et al.: GROWING A TREE FROM DECISION REGIONS ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2010-BMVC-supertree.pdf
    13 Mar 2018: The regions are represented by the boolean table and theboolean expression is minimised (middle). ... Caltech bg datasetMPEG-7 f ace data. BANCA f ace set. MITCMU f ace test set.
  16. PRONUNCIATION MODELING BY SHARING GAUSSIAN DENSITIESACROSS PHONETIC…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro99.pdf
    9 Aug 2005: Steps 6, 3, 4 and 5 are then carried out to estimateand test a pronunciation model. ... in Step 5 (Test) PER WER PER WERNone (Dictionary) 49.1% 49.1% 49.5% 58.9%Tree Pron.
  17. Efficient Large-Scale Semantic VisualLocalization in 2D Maps Tomas…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2020-ACCV-Large-scale-localization.pdf
    28 Jun 2021: database of embeddings of the test environmentlocations to retrieve the most similar location. ... The plots in the middle show mean confidence of trajectories w.r.t.
  18. Multilingual Models in Neural Machine Translation

    mi.eng.cam.ac.uk/~wjb31/MPhil_Thesis_Guangyu_Yang.pdf
    21 Nov 2023: processing. 29. 4.5 Examples of translation hypotheses from the test set of WMT’21 Chinese-English. ... In this project, we test the impact of using 2 to 32 demonstrationexamples.
  19. Learning to Track with Multiple Observers Björn StengerComputer…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2009-CVPR-hand-trackingpdf
    28 Jul 2009: 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
  20. 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.
  21. 20 Feb 2018: The training data con-tains 2207 dialogues and the test set consistsof 1117 dialogues. ... For goals, the gains are always statis-tically significant (paired t-test, p < 0.05).
  22. Face Recognition with Image Sets Using Manifold Density Divergence ...

    mi.eng.cam.ac.uk/reports/svr-ftp/oa214_CVPR_2005_paper1.pdf
    8 Aug 2005: 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.
  23. LOGOTHETIS ET AL.: A CNN BASED APPROACH FOR THE ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2020-BMVC-CNN-NF-Photometric-Stereo.pdf
    28 Jun 2021: As the depth will onlybe approximately known at test time, this is slightly perturbed before mappingresulting to a structured change of the map (shown at bottom left: middle is actualdepth (10 ... In order to solve the near-field PS problem for aspecific
  24. X-MAN: Explaining multiple sources of anomalies in video Stanislaw ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-CVPR-XMAN-anomaly-detection.pdf
    9 Apr 2022: Bottom: Test frame detected as anomalous, showingat least one HOI vector has low probability under the GMM. ... Evaluation metric. All test video frames from alldatasets are marked as either containing or not containing ananomaly.
  25. Discrete neural representations for explainable anomaly detection…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2022-WACV-anomaly-detection.pdf
    13 Mar 2023: Anomaly detection metric. All test video frames from alldatasets are marked as either containing or not containing ananomaly. ... Middle column shows thesaliency maps produced by passing the input frame through theprediction network.
  26. Shape Context and Chamfer Matching in Cluttered Scenes A. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_cvpr03.pdf
    12 May 2003: Point correspondences betweentwo shapesare found by minimizing the point matchingcosts,which is the test statistic for histograms. ... 7. Figure 5: Results of hand localization. Left column: handlocalization usingshapecontext informationonly
  27. KIM et al.: GROWING A TREE FROM DECISION REGIONS ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2010-BMVC-supertree.pdf
    13 Mar 2018: The regions are represented by the boolean table and theboolean expression is minimised (middle). ... Caltech bg datasetMPEG-7 f ace data. BANCA f ace set. MITCMU f ace test set.
  28. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-ICCV-relocalisation-arXiv.pdf
    13 Mar 2018: This noveldataset provides data to train and test pose regression algo-rithms in a large scale outdoor urban setting. ... Train and test im-ages are taken from distinct walking paths and not sampledfrom the same trajectory making the regression
  29. The Layout Consistent Random Field for Recognizing and Segmenting ...

    mi.eng.cam.ac.uk/reports/svr-ftp/shotton_cvpr06.pdf
    3 Apr 2006: and used for training wereclean, single-instance images, and so our performance overthe 150 remaining test images would be therefore expectedto be slightly lower than for the whole database. ... Left: input image. Middle: inferred parts la-bellings.
  30. IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...

    mi.eng.cam.ac.uk/research/projects/AGILE/publications/mjfg_ASL.pdf
    23 Feb 2006: The various models andrecognition configurations are evaluated using several recent BNdevelopment and evaluation test sets. ... Test set # Shows Hours Period. dev03 6 3 Jan. 2001eval03 6 3 Feb.
  31. 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
  32. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera…

    mi.eng.cam.ac.uk/~cipolla/archive/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.
  33. BBC News - Silicon Fen shows off its wares at Cambridge showcase

    mi.eng.cam.ac.uk/~cipolla/archive/Public-Understanding/2011-BBC-Zappar.pdf
    7 Nov 2014: Her product can quickly test the way people walk by means of gyroscopes and ac-celerometers, stored in sensors strapped to the legs. ... Mrs Hodgins has witnessed many economic cycles. "Innovation at this time is one of the hardest things to do," she says
  34. LOGOTHETIS ET AL.: A CNN BASED APPROACH FOR THE ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2020-BMVC-CNN-NF-Photometric-Stereo.pdf
    28 Jun 2021: As the depth will onlybe approximately known at test time, this is slightly perturbed before mappingresulting to a structured change of the map (shown at bottom left: middle is actualdepth (10 ... In order to solve the near-field PS problem for aspecific
  35. 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: Test-ing was performed on 164 images containing 193 cars,and164 background images. ... Our technique relies on edge features andhigher-resolution training and test images would certainlyimprove results.
  36. Uniform precision ultrasound strain imaging G.M. Treece, J.E. Lindop, …

    mi.eng.cam.ac.uk/reports/svr-ftp/treece_tr624.pdf
    9 Mar 2009: The remaining images show lightlyfiltered (middle row) and heavily filtered (bottom row) versions of the strain data. ... The remainingimages show lightly filtered (middle row) and heavily filtered (bottom row) versions of the straindata.
  37. Semantic Transform: Weakly Supervised Semantic Inference for Relating …

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2013-ICCV-Shankar-attrirbutes.pdf
    13 Mar 2018: Givena test image with its feature vector xt, its score for attributeam is given by ϕ(bTm, xt). ... Absolute Classification: This refers to the task of as-signing a test image to its correct class.
  38. main.dvi

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2011-PAMI-3D-Faces.pdf
    13 Mar 2018: to test our hypothesis. Therefore the problem of detecting. shadows becomes more difficult. ... The shading regularization scheme shows a smooth surface. (Fig. 5 middle) while the shape regularization scheme (Fig.
  39. 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.
  40. PX-NET: Simple and Efficient Pixel-Wise Trainingof Photometric Stereo …

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-ICCV-PX-NET-photometric-normals.pdf
    9 Apr 2022: performs realistic computation of global illumination ef-fects. The first dataset is Cycles-PS-Test [16] containing 3objects. ... For completeness, we also in-clude the results after applying the test time rotation pseudo-invariance augmentation (K=10).
  41. Reducing Word Error Rates of Found Speech -XPERT Tool ...

    mi.eng.cam.ac.uk/reports/svr-ftp/johnson_tr330.pdf
    10 Apr 2000: Middle-clicking on a word in the recogniser output focuses on the word plus two words of context oneither side, giving 5 words in total. ... The speech is spontaneous and suffersfrom a large variation in speaker rate, with un-natural gaps in the middle
  42. A Pose-Wise Linear Illumination Manifold Model for Face Recognition…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2007_CVIU_paper2.pdf
    13 Mar 2018: also see [27]). In AFR tests, such methods are usually outperformed by meth-. ... the data as test input. In all tests, both training data for each person in the gallery,.
  43. hci09.dvi

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2009-HCI-Stenger.pdf
    13 Mar 2018: Figure 2: Appearance variation of hand regions. Shown. are cropped hand regions from test sequences. ... pair from NCC to CM due to motion blur, middle pair from CM.
  44. tr.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/prager_tr436.pdf
    28 Jun 2002: 1000of an inch. apart. In the second recording, 100 slices were recorded 0.02 mm apart.After decompression, a patch of 147 119 pixels was extracted from the middle of. ... Determination of scan-plane motion using speckle decorrelation: theoretical
  45. Large scale labelled video data augmentation for semantic…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2017-ICCV-label-propagation.pdf
    13 Mar 2018: We obtained propagated labels for all the images in thetrain, test and validation datasets, however we use only thelabels in the training dataset in the experiments describedhere. ... or Hand labels. The graph on the right shows class accuracy and IoU
  46. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2015-ICCV-relocalisation-arXiv.pdf
    13 Mar 2018: This noveldataset provides data to train and test pose regression algo-rithms in a large scale outdoor urban setting. ... Train and test im-ages are taken from distinct walking paths and not sampledfrom the same trajectory making the regression
  47. Shadows in three-source photometric stereo Carlos Hernández1 George…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-ECCV-faces.pdf
    13 Mar 2018: 2 Hernández et al. test our hypothesis. Therefore the problem of detecting shadows becomes moredifficult. ... 3 top. Thenormalized images ci‖c‖ in the middle of Fig. 3 allow the algorithm to easily detect.
  48. Ghostscript wrapper for…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-CVPR-Kim-tensor.pdf
    13 Mar 2018: correlations chosen from the list is performed to categorize. a new test video. ... scenarios. Leave-one-out cross-validation was performed. to test the proposed method, i.e.
  49. Int J Comput Vis (2012) 100:203–215DOI 10.1007/s11263-011-0461-z…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2012-IJCV-Shallow-trees.pdf
    13 Mar 2018: pression is minimised (middle). An optimal short tree is built on theminimum expression (right). ... 8(left) and Fig. 9(left). The six test sets werecreated by randomly perturbing the train sets.
  50. Discrete neural representations for explainable anomaly detection…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-WACV-anomaly-detection.pdf
    13 Mar 2023: Anomaly detection metric. All test video frames from alldatasets are marked as either containing or not containing ananomaly. ... Middle column shows thesaliency maps produced by passing the input frame through theprediction network.
  51. Model-Based 3D Tracking of an Articulated Hand B. Stenger ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2001-CVPR-Stenger-hand.pdf
    13 Mar 2018: Top and middle row: model pro-jected on images from camera 1 and 2, respectively. ... 4. Experimental Results. Real data experiments were designed to test the proposedtracking algorithm.

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