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

IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules