Joint Object Contour Points and Semantics for Instance Segmentation
This work addresses instance segmentation for computer vision applications, but it is incremental as it builds upon Mask R-CNN with an auxiliary task.
The paper tackled the problem of instance segmentation by improving attention to object boundaries, resulting in performance gains of 3.8% on Cityscapes and 0.8% on COCO compared to vanilla Mask R-CNN.
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 object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network's attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8\% on Cityscapes dataset and 0.8\% on COCO dataset.