Search

20 Feb 2018: Reducingthenumber of system components leads to a simpler architecturethat iseasier to implement and test. ... Table 4. Analysis of detected speech segments using V2 and A2models on noisy test setGM2b.

13 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).

20 Feb 2018: Contrasts marked are statisticallysignificant (p < 0.05) using a Kruskal-Wallis rank sum test. ... Test System Num Objective Success Rate Perceived Average WERDialogs Partial Full Success Rate Turns.

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.

13 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).

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

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.

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.

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.

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

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.

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).

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.

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

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.

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.

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

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

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 Feb 2018: policy. In the regions of belief space close to the corners (where certainty is high), the policy chooses doSave or doDelete; in the middle of belief space (where certainty is low)

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.

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.

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.

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.

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,.

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.

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

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

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.

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.

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.

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.

13 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.

13 Mar 2018: Middle) The clusters of region trajectories computed with our algorithm are represented with differentshaded colours (some colours may be repeated). ... 1. To limit the computational load we con-sider the first 100 frames only.Our main purpose is to test

20 Feb 2018: marks statistically sig-nificant difference between RT S and RIQ (p < 0.05, T-test). ... Figure 3: Moving TSR (left), moving AIQ (middle) and moving AUS (right) for using either TS, IQ, or US as reward averaged over twopolicies respectively, computed on

13 Mar 2018: In AFR tests, such methods are usuallyoutperformed by methods from the third class: view-based techniques e.g. ... KLD) [38]. In all tests, both training data for each person in the gallery, as well as test data,consisted of only a single sequence.

13 Mar 2018: 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

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.

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).

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,.

13 Mar 2018: Figure 7. Sample of sparse reconstruction output. Top: a long reconstructed section; middle: close-. ... detection system and test it more extensively, comparing. the inspection performance between manual, fully-.

13 Mar 2018: The class that exhibits the maximum frequency in the histogram is as-signed to the test video. ... The train/test split is around 50% and the videos arechosen as specified in [17].

13 Mar 2018: Figure 1. A building façade (left), the edgels output from Canny (middle) and the resulting straightline-segments (right). ... 5 EvaluationTo test this system, a database was constructed from photographs of all the buildings in the main shoppingstreet

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.

13 Mar 2018: avoid bias), resulting in a total of 600 pairwise compar-isons per preference test. ... Scores shown as percentages of all votes for: (left) all emotions, (middle)neutral, and (right) angry.

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.

13 Mar 2018: InAFR tests, such methods are usually outperformed by methodsfrom the third class: view-based techniques e.g. ... These were used for neither gallery data nor test inputfor the evaluation reported in this section.

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.

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.

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