Region-based segmentation and object detection

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Current computer vision models for image segmentation and object detection use awkward feature mappings and uncoupled subtask representations making joint inference clumsy and leaving the classification of many parts of the scene ambiguous. Gould et al. propose combining image segmentation and object detection in a hierarchical model to reason simultaneously about pixels, regions, and objects

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