# 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.

# U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

18 May 2020

In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).

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# Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

The latent spaces of GAN models often have semantically meaningful directions.

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# Pyramid Feature Attention Network for Saliency detection

To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features.

276

# Deeply supervised salient object detection with short connections

Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).

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# PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection

We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.

167

# Uncertainty Inspired RGB-D Saliency Detection

7 Sep 2020

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.

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# UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.

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# Real Time Image Saliency for Black Box Classifiers

In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier.

113

# Object Segmentation Without Labels with Large-Scale Generative Models

8 Jun 2020

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.

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