Saliency Prediction
88 papers with code • 3 benchmarks • 7 datasets
A saliency map is a model that predicts eye fixations on a visual scene. Saliency prediction is informed by the human visual attention mechanism and predicts the possibility of the human eyes to stay in a certain position in the scene.
Benchmarks
These leaderboards are used to track progress in Saliency Prediction
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Libraries
Use these libraries to find Saliency Prediction models and implementations
SALICON
iSUN
UIEB
SUIM
MSU Video Saliency Prediction
Salient-KITTI
Most implemented papers
Contextual Encoder-Decoder Network for Visual Saliency Prediction
To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications.
Uncertainty Inspired RGB-D Saliency Detection
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.
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
Reverse Attention for Salient Object Detection
ShuhanChen/RAS_ECCV18
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ECCV 2018
Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently.
Fast Underwater Image Enhancement for Improved Visual Perception
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement.
BASNet: Boundary-Aware Salient Object Detection
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
A Deep Multi-Level Network for Saliency Prediction
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.
