Saliency Detection
82 papers with code • 6 benchmarks • 12 datasets
Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
Source: An Unsupervised Game-Theoretic Approach to Saliency Detection
Benchmarks
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PASCAL-S
HKU-IS
DUTS
DUT-OMRON
CAT2000
iSUN
ECSSD
Lytro Illum
HS-SOD
ReDWeb-S
Greatest papers with code
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on
Salient Object Detection
on HKU-IS
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
anvoynov/GANLatentDiscovery
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ICML 2020
The latent spaces of GAN models often have semantically meaningful directions.
Pyramid Feature Attention Network for Saliency detection
CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
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CVPR 2019
To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features.
Ranked #1 on
Saliency Detection
on DUTS-test
Deeply supervised salient object detection with short connections
Andrew-Qibin/DSS
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CVPR 2017
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).
Ranked #4 on
RGB Salient Object Detection
on SBU
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Ugness/PiCANet-Implementation
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CVPR 2018
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
Ranked #7 on
RGB Salient Object Detection
on SOC
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.
Ranked #1 on
RGB Salient Object Detection
on SOC
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
JingZhang617/UCNet
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CVPR 2020
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Ranked #4 on
RGB-D Salient Object Detection
on LFSD
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection
We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation.
Real Time Image Saliency for Black Box Classifiers
PiotrDabkowski/pytorch-saliency
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NeurIPS 2017
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier.
Object Segmentation Without Labels with Large-Scale Generative Models
anvoynov/BigGANsAreWatching
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The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks.