A saliency map is a model that predicts eye fixations on a visual scene.
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #2 on RGB Salient Object Detection on ECSSD
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on Multi-Human Parsing on MHP v2.0
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research effort over the years.
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles.
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations.
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
Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps.