Towards Unified Object Detection and Semantic Segmentation

Abstract

Object detection and semantic segmentation are two strongly correlated tasks, yet typically solved separately or sequentially with substantially different techniques. Motivated by the complementary effect observed from the typical failure cases of the two tasks, we propose a unified framework for joint object detection and semantic segmentation. By enforcing the consistency between final detection and segmentation results, our unified framework can effectively leverage the advantages of leading techniques for these two tasks. Furthermore, both local and global context information are integrated into the framework to better distinguish the ambiguous samples. By jointly optimizing the model parameters for all the components, the relative importance of different component is automatically learned for each category to guarantee the overall performance. Extensive experiments on the PASCAL VOC 2010 and 2012 datasets demonstrate encouraging performance of the proposed unified framework for both object detection and semantic segmentation tasks.