ITM Web Conf.
Volume 60, 20242023 5th International Conference on Advanced Information Science and System (AISS 2023)
|Number of page(s)
|09 January 2024
Correspondence Learning via Correspondence Embedded and Channel Recalibration Network
1 School of Software Engineering, Tongji University, Shanghai, 201804, China
2 Shanghai ideal Information Industry (Group) Co., LTD, Shanghai, 201315, China
* Corresponding author: email@example.com
Correspondence learning is pivotal to many computer vision-based tasks. Existing methods regard each correspondence equally along the channel dimension, which weakens the feature representation capability of the network. To alleviate this problem, we propose a Correspondence Embedded and Channel Recalibration Network, named CECR-Net, to predict the inlier probability of each correspondence and recover camera poses. The proposed CECR-Net is designed to explore the potential impact of correspondences on the channel dimension, and recalibrate the weight of each channel, so that our CECRNet can capture more exact contextual information. Experiments show that our CECR-Net is effective in outlier removal and camera pose estimation tasks on challenging public datasets.
© The Authors, published by EDP Sciences, 2024
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