Abstract
The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the target edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point RCNN aiming at promoting the neural networks attention to the target edge information, which can heighten the information propagates between multiple tasks by using different attributes features. Specifically, we present an auxiliary task to Mask RCNN, including utilizing keypoint detection technology to construct the target edge contour, and enhancing the sensitivity of the network to the object edge through multi task learning and feature fusion. These improvements are easy to implement and have a small amount of additional computing overhead. By extensive evaluations on the Cityscapes dataset, the results show that our approach outperforms vanilla Mask RCNN by 5.4 on the validation subset and 5.0 on the test subset.
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URL
https://arxiv.org/abs/2008.00460
