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.
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
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
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
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
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
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
We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation.
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier.
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.