We present a simple approach for producing a small\u00a0number of structured visual outputs which have high recall,\u00a0for a variety of tasks including monocular pose estimation\u00a0and semantic scene segmentation. Current state-of-the-art\u00a0approaches learn a single model and modify inference procedures to produce a small number of diverse predictions.\u00a0We take the alternate route of modifying the learning procedure to directly optimize for good, high recall sequences of\u00a0structured-output predictors. Our approach introduces no\u00a0new parameters, naturally learns diverse predictions and is\u00a0not tied to any specific structured learning or inference procedure. We leverage recent advances in the contextual submodular maximization literature to learn a sequence of predictors and empirically demonstrate the simplicity and performance of our approach on multiple challenging vision\u00a0tasks including achieving state-of-the-art results on multiple predictions for monocular pose-estimation and image\u00a0foreground\/background segmentation.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a simple approach for producing a small\u00a0number of structured visual outputs which have high recall,\u00a0for a variety of tasks including monocular pose estimation\u00a0and semantic scene segmentation. Current state-of-the-art\u00a0approaches learn a single model and modify inference procedures to produce a small number of diverse predictions.\u00a0We take the alternate route of modifying the learning procedure […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"dedey"}],"msr_publishername":"International Conference on Computer 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