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|a 10.1109/TPAMI.2024.3444457
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|a eng
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|a Xiao, Guobao
|e verfasserin
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|a T-Net++
|b Effective Permutation-Equivariance Network for Two-View Correspondence Pruning
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|c 2024
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|a Text
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|a Date Revised 08.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We propose a conceptually novel, flexible, and effective framework (named T-Net++) for the task of two-view correspondence pruning. T-Net++ comprises two unique structures: the "-'' structure and the "|'' structure. The "-'' structure utilizes an iterative learning strategy to process correspondences, while the "|'' structure integrates all feature information of the "-'' structure and produces inlier weights. Moreover, within the "|'' structure, we design a new Local-Global Attention Fusion module to fully exploit valuable information obtained from concatenating features through channel-wise and spatial-wise relationships. Furthermore, we develop a Channel-Spatial Squeeze-and-Excitation module, a modified network backbone that enhances the representation ability of important channels and correspondences through the squeeze-and-excitation operation. T-Net++ not only preserves the permutation-equivariance manner for correspondence pruning, but also gathers rich contextual information, thereby enhancing the effectiveness of the network. Experimental results demonstrate that T-Net++ outperforms other state-of-the-art correspondence pruning methods on various benchmarks and excels in two extended tasks
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|a Liu, Xin
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|a Zhong, Zhen
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|4 aut
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| 700 |
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|a Zhang, Xiaoqin
|e verfasserin
|4 aut
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| 700 |
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|a Ma, Jiayi
|e verfasserin
|4 aut
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| 700 |
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|a Ling, Haibin
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
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|g 46(2024), 12 vom: 16. Dez., Seite 10629-10644
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|u http://dx.doi.org/10.1109/TPAMI.2024.3444457
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