Multi-level progressive parallel attention guided salient object detection for RGB-D images
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The attention mechanism has gained wide adoption in computer vision due to its spectral capabilities for multimodal applications such as video description and salient object detection.
“Since its introduction, the AM has gained wide adoption in the computer vision community due to its spectral capabilities for many multimodal applications such as video description [233, 234], salient object detection [235], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [235]
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References 51 · Cited by 16
- Authors
- Liu, Z., Duan, Q., Shi, S., & Zhao, P.
- Affiliations
- Anhui University (CN)
- Corresponding author
- Liu, Zhengyi
- Venue
- The Visual Computer
- Published
- 2020-02-18
- Volume
- 37
- Issue
- 3
- Pages
- 529-540
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3007241528
- Cited by
- 16
- References
- 51
- Citations by year
- 2025: 1 · 2023: 5 · 2022: 4 · 2021: 4 · 2020: 2
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Funders
- Natural Science Foundation of Anhui Province (1908085MF182)
- National Natural Science Foundation of China (61602004)
- Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2019A0034)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- salient object detection, RGB-D, spatial attention, channel attention, multi-scale feature fusion, depth information, progressive optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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