UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Open accessAbstract
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic learning pipeline. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. With the proposed saliency consensus process, we are able to generate an accurate saliency map based on these multiple predictions. Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps, leading to a new state-of-the-art in RGB-D saliency detection.
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In 2020, Wang et al. [649] used CNN with SCN: Self-Cure Network for facial expression on RAF-DB, FERPlus, AffectNet, and WebEmotion.
“Wang al. [649] | et | 2020 | RAF-DB, FERPlus, AffectNet and WebE- motion | Facial expression | # RAF-DB: 30,000 different facial images, FERPlus: 28,709, 3,589 and 3,589 images for training , validation and test, respectively, AffectNet: 450,000 images, WebEmotion: 41,000 videos | N/A | CNN | SCN:”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [649]
The Setup section (p. not available) describes experiments on six RGB-D saliency datasets, not the facial-expression datasets in the claim. Searches for Self-Cure Network, SCN, facial expression, and AffectNet found no passage in our copy addressing the attributed method or experiments. (LLM, conf=0.78)
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- Authors
- Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N.
- Affiliations
- Australian National University (AU)
- Nankai University (CN)
- Northwestern Polytechnical University (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8579-8588
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035633116
- Cited by
- 423
- References
- 67
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 82 · 2023: 89 · 2022: 88 · 2021: 71 · 2020: 35
- FWCI
- 21.82
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61871325, 61420106007, 61671387)
- Australian Centre for Robotic Vision (#CE140100016)
- National Key Research and Development Program of China (2018AAA0102803)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- uncertainty modeling, conditional variational autoencoder, latent space sampling
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