126 papers with code • 7 benchmarks • 13 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).
To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features.
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).
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.
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.