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13 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.

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: 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.

13 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

20 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

20 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.

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: 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 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.

13 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.

20 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.

20 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

13 Mar 2018: We test the performance of SegNet on outdoorRGB scenes from CamVid, KITTI and indoor scenes fromthe NYU dataset. ... Features based on appearance[32], SfM and appearance [2, 36, 20] have been explored forthe CamVid test.

20 Feb 2018: All TSR differences are statis-tically significant (t-test, p < 0.05). 4,000 dialogues in 10 batches.

13 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.

13 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

13 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.

13 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.

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.

20 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