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of 137
pro vyhledávání: '"Du, Songlin"'
Recent advancements in keypoint detection and descriptor extraction have shown impressive performance in local feature learning tasks. However, existing methods generally exhibit suboptimal performance under extreme conditions such as significant app
Externí odkaz:
http://arxiv.org/abs/2407.15791
Autor:
Lu, Xiaoyong, Du, Songlin
Current feature matching methods prioritize improving modeling capabilities to better align outputs with ground-truth matches, which are the theoretical upper bound on matching results, metaphorically depicted as the "ceiling". However, these enhance
Externí odkaz:
http://arxiv.org/abs/2407.07789
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challeng
Externí odkaz:
http://arxiv.org/abs/2308.09949
Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks
Externí odkaz:
http://arxiv.org/abs/2303.00941
Publikováno v:
In Pattern Recognition December 2024 156
Publikováno v:
In Pattern Recognition November 2024 155
Bottom-up based multi-person pose estimation approaches use heatmaps with auxiliary predictions to estimate joint positions and belonging at one time. Recently, various combinations between auxiliary predictions and heatmaps have been proposed for hi
Externí odkaz:
http://arxiv.org/abs/2110.10734
Publikováno v:
In Pattern Recognition June 2024 150
Publikováno v:
In Pattern Recognition March 2024 147
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