Fusion Scheme for Semantic and Instance-level Segmentation

A powerful scene understanding can be achieved by combining the tasks of semantic segmentation and instance level recognition. Considering that these tasks are complementary, we propose a multi-objective fusion scheme which leverages the capabilities of each task: pixel level semantic segmentation performs well in background classification and delimiting foreground objects from background, while instance level segmentation excels in recognizing and classifying objects as a whole. We use a fully convolutional residual network together with a feature pyramid network in order to achieve both semantic segmentation and Mask R-CNN based instance level recognition. We introduce a novel heuristic fusion approach for panoptic segmentation. The instance and semantic segmentation output of the network is fused into a panoptic segmentation. This is achieved using object sub-category class and instance propagation guidance by object category class from semantic segmentation. The proposed solution achieves significant improvements in semantic object segmentation and object mask boundaries refinement at low computational costs.