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CONTEXT
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Learned Satellite Clusters
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Results - Satellite Prior: Detector Only Posterior: TAS Model Region Labels
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Results - Satellite
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Results – PASCAL Good examples –Discover “true positives” –Remove “false positives”
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Results – VOC 2005 Motorbike Car Bicycle People
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Results – VOC 2006 Cow Sheep
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LOOPS
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Giraffe 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples LOOPS + Logistic Centroid Naive Bayes GROUND + Logistic
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Giraffe 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples LOOPS + Logistic LOOPS + NN LOOPS + SVM
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Cheetah 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Cheetah 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Airplane 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Airplane 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Lamp Shade 0246810 0.4 0.6 0.8 1 LOOPS + Logistic Centroid Naive Bayes GROUND + Logistic Classification Accuracy # Training Examples
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Lamp Shade 0246810 0.4 0.6 0.8 1 LOOPS + Logistic LOOPS + NN LOOPS + SVM Classification Accuracy # Training Examples
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Lamp Base 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Lamp Base 0246810 0.4 0.6 0.8 1 Classification Accuracy # Training Examples
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Overlap - Caltech AirplaneBassBuddhaRooster 0.5 0.6 0.7 0.8 0.9 1 Centroid LOOPS Overlap Score
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Overlap - Mammals BisonDeerEle. Gir. 0.5 0.6 0.7 0.8 0.9 1 Overlap Score LlamaRhino
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0.3 0.4 0.5 0.6 0.7 0.8 Centroid Landmark LOOPS Landmark Success AirplaneBassBuddhaRooster
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0.3 0.4 0.5 0.6 0.7 0.8 BisonDeerEle. Gir. LlamaRhino Landmark Success
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-1 std +1 std Mean Correspondence (a) Section 3 Simple Training Outlines Corresponded Outlines LOOPS Model Localized Test Outlines Descriptive Classification UP DOWN Boosted Detectors (b) Section 4.2 Shape Model (b) Section 4.1 Discrete Inference (c) Section 5 Refinement (c) Section 5 Shape Classification (d) Section 6.2 DownUp
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SHAPE COLOR
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Full Airplane ClusterZoomed in Tails
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Tail Cluster (White)Tail Cluster (Red)
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