Object detection and multi-class image segmentation are two closely related tasks that can be greatly improved when solved jointly by feeding information from one task to the other. However, current state-of-the-art models use a separate representation for each task making joint inference clumsy and leaving classification of many parts of the scene ambiguous. In this work, we propose a hierarchical region-based approach to joint object detection and image segmentation. Our approach reasons about pixels, regions and objects in a coherent probabilistic model. Importantly, our model gives a single unified description of the scene. We explain every pixel in the image and enforce global consistency between all variables in our model. We run experiments on challenging vision datasets and show significant improvement over state-of-the-art object detection accuracy.