RGB Salient Object Detection
97 papers with code • 13 benchmarks • 17 datasets
RGB Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or RGB images.
( Image credit: Attentive Feedback Network for Boundary-Aware Salient Object Detection )
Benchmarks
These leaderboards are used to track progress in RGB Salient Object Detection
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Libraries
Use these libraries to find RGB Salient Object Detection models and implementations
PASCAL-S
DUTS
HKU-IS
DUT-OMRON
ISTD
SBU
ECSSD
SOC
VT5000
HRSOD
Latest papers
Regularized Densely-connected Pyramid Network for Salient Instance Segmentation
yuhuan-wu/RDPNet
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Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.
Siamese Network for RGB-D Salient Object Detection and Beyond
Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.
Label Decoupling Framework for Salient Object Detection
Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution.
Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
ShuhanChen/PGAR_ECCV20
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ECCV 2020
In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
guotaowang/STVS
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In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
Depth Quality Aware Salient Object Detection
qdu1995/DQSD
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The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
qduOliver/MQP
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Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
