Methods › Computer Vision › RGB-D Saliency Detection Models › UCNet
UCNet
Introduced by Jing Zhang et al. in UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
UCNet is a probabilistic framework for RGB-D Saliency Detection that employs uncertainty by learning from the data labelling process. It utilizes conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space.
Papers archive 2025-07-28
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UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders 13 Apr 2020 · 1 repository · arXiv:2004.05763
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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