Joint Object Contour Points and Semantics for Instance Segmentation
- URL: http://arxiv.org/abs/2008.00460v3
- Date: Sun, 4 Jul 2021 02:21:19 GMT
- Title: Joint Object Contour Points and Semantics for Instance Segmentation
- Authors: Wenchao Zhang, Chong Fu, Mai Zhu
- Abstract summary: 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.
As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features.
- Score: 1.2117737635879038
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 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.
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