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While there has been significant progress in solving the problems of imagepixel labeling, object detection and scene classification, existing approachesnormally address them separately. In this paper, we propose to tackle theseproblems from a bottom-up perspective, where we simply need a semanticsegmentation of the scene as input. We employ the DeepLab architecture, basedon the ResNet deep network, which leverages multi-scale inputs to later fusetheir responses to perform a precise pixel labeling of the scene. This semanticsegmentation mask is used to localize the objects and to recognize the scene,following two simple yet effective strategies. We evaluate the benefits of oursolutions, performing a thorough experimental evaluation on the NYU Depth V2dataset. Our approach achieves a performance that beats the leading results bya significant margin, defining the new state of the art in this benchmark forthe three tasks comprising the scene understanding: semantic segmentation,object detection and scene categorization.

In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization