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
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection
To overcome these, we propose the M$^3$Net, i. e., the Multilevel, Mixed and Multistage attention network for Salient Object Detection (SOD).
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
plemeri/transparent-background
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Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
rmcong/Hybrid-Label-SOD_TCSVT2022
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In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
TRACER: Extreme Attention Guided Salient Object Tracing Network
Karel911/TRACER
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Existing studies on salient object detection (SOD) focus on extracting distinct objects with edge information and aggregating multi-level features to improve SOD performance.
Disentangled High Quality Salient Object Detection
As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions.
P2T: Pyramid Pooling Transformer for Scene Understanding
yuhuan-wu/P2T
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A popular solution to this problem is to use a single pooling operation to reduce the sequence length.
Densely Deformable Efficient Salient Object Detection Network
tanveer-hussain/EfficientSOD
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In this paper, inspired by the best background/foreground separation abilities of deformable convolutions, we employ them in our Densely Deformable Network (DDNet) to achieve efficient SOD.
Uncertainty Inspired RGB-D Saliency Detection
Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.
