Methods › Computer Vision › Convolutional Neural Networks › VoVNetV2
VoVNetV2
Introduced by Youngwan Lee et al. in CenterMask : Real-Time Anchor-Free Instance Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
VoVNetV2 is a convolutional neural network that improves upon VoVNet with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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CenterMask : Real-Time Anchor-Free Instance Segmentation 15 Nov 2019 · 8 repositories · arXiv:1911.06667Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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