A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation
This paper proposes a fusion based method to generate pseudo annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low level features and own local information, which is hard to generate accurate and diverse results for the fusion. Different from the traditional methods, multiple class agnostic models are modeled to learn the objectiveness cues by using existing labeled pixel level annotations and then to fuse. Firstly, the classical Fully Convolutional Network (FCN) that densely predicts the pixels' labels is used. Then, two new sparse prediction based class agnostic models are proposed, which simplify the segmentation task as sparsely predicting the boundary points through predicting the distance from the bounding box border to the object boundary in Cartesian Coordinate System and the Polar Coordinate System, respectively. Finally, a voting based strategy is proposed to combine these segmentation results to form better pseudo annotations. We conduct experiments on PASCAL VOC 2012 dataset. The mIoU of the proposed method is 68.7% which outperforms the state of the art method by 1.9%
2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)