Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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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 PASCAL-S
The latent spaces of GAN models often have semantically meaningful directions.
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
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
Ranked #5 on RGB Salient Object Detection on DUTS-TE
In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.
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 HKU-IS
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 #2 on RGB-D Salient Object Detection on STERE
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier.
However, since generative models are known to be unstable and sensitive to hyperparameters, the training of these methods can be challenging and time-consuming.
In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection.
Ranked #11 on RGB-D Salient Object Detection on NJU2K (using extra training data)