F-Measure (), MAE (), S-Measure (), and E-Measure () were compared quantitatively with the RGB-D methods on six widely used RGB-D datasets.

F-Measure (), MAE (), S-Measure (), and E-Measure () were compared quantitatively with the RGB-D methods on six widely used RGB-D datasets.

<p>The best results are shown in bold black. A "-" symbol represents that the code or result is unavailable.</p>

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Bibliographic Details
Main Author: Runqing Li (1827358) (author)
Other Authors: Ling Yu (330944) (author), Zijian Jiang (3411935) (author), Fanglin Niu (6188366) (author)
Published: 2025
Subjects:
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
spatial layout information
salient object detection
recent years due
model &# 8217
dimensional dynamic convolution
convolutional neural networks
highlighting salient objects
garnered significant attention
extracting local details
texture details
small objects
attention mechanism
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rely solely
relate high
prediction mapping
named cignet
modal interactive
mcfm ),
level features
geometric morphology
excellent performance
depth images
depth features
complex scenes
cnns ),
accurately recognising
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Description
Summary:<p>The best results are shown in bold black. A "-" symbol represents that the code or result is unavailable.</p>

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