supervised adptive threshold network for instance segmentation
This work addresses instance segmentation for computer vision applications, but it appears incremental as it builds directly on Mask R-CNN with adaptive modifications.
The paper tackles the problem of information propagation defects in instance segmentation models like Mask R-CNN by proposing a supervised adaptive threshold network that uses adaptive binarization and an adaptive feature pool to improve segmentation and reduce errors, with experiments on benchmark datasets indicating its effectiveness.
Currently, instance segmentation is attracting more and more attention in machine learning region. However, there exists some defects on the information propagation in previous Mask R-CNN and other network models. In this paper, we propose supervised adaptive threshold network for instance segmentation. Specifically, we adopt the Mask R-CNN method based on adaptive threshold, and by establishing a layered adaptive network structure, it performs adaptive binarization on the probability graph generated by Mask RCNN to obtain better segmentation effect and reduce the error rate. At the same time, an adaptive feature pool is designed to make the transmission between different layers of the network more accurate and effective, reduce the loss in the process of feature transmission, and further improve the mask method. Experiments on benchmark data sets indicate that the effectiveness of the proposed model