Active instance segmentation with fractional-order network and reinforcement learning

In this paper, a novel model is proposed to segment image instance based on fractional-order chaotic synchronization network and reinforcement learning method. In the proposed model, fractional-order network is used for the preliminary image segmentation, which can obtain fine-grained information to provide a guiding strategy for the exploration of reinforcement learning; afterward, reinforcement learning method is committed to generate high-quality bounding contour curves for the object instances, which can combine the pixel features with local information in the image to improve the overall accuracy. Compared with other fractional-order models, the experimental results show that our proposed model achieves higher accuracy on the datasets of Pascal VOC2007 and Pascal VOC2012.

Recommended citation: Li, X., Wu, G., Zhou, S., Lin, X., & Li, X. (2022). Active instance segmentation with fractional-order network and reinforcement learning. The Visual Computer, 1-14.
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