MESAC: Learning to Remove Mismatches via Maximizing the Expected Score of Sample Consensuses

MESAC: Learning to Remove Mismatches via Maximizing the Expected Score of Sample Consensuses

Most learning-based methods require labelling the training data, which is time-consuming and gives rise to wrong labels. To address the labelling issues thoroughly, we propose an unsupervised learning framework to remove mismatches by maximizing the expected score of sample consensuses (MESAC). The...

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Bibliographic Details
Main Authors: Shiyu Chen, Cailong Deng, Yong Zhang, Yong Wang, Qixin Zhang, Feiyan Chen, Zhimin Zhou
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Mismatch removal
permutation invariant network
categorical distribution
gradient explosion
score-function estimator
Online Access:https://ieeexplore.ieee.org/document/10138163/

Internet

https://ieeexplore.ieee.org/document/10138163/

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