  Driven by Convolutional Neural Networks, object detection and semantic
segmentation have gained significant improvements. However, existing methods on
the basis of a full top-down module have limited robustness in handling those
two tasks simultaneously. To this end, we present a joint multi-task framework,
termed IvaNet. Different from existing methods, our IvaNet backwards abstract
semantic information from higher layers to augment lower layers using local
top-down modules. The comparisons against some counterparts on the PASCAL VOC
and MS COCO datasets demonstrate the functionality of IvaNet.
