Saliency Prediction
87 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Saliency Prediction models and implementations
SALICON
iSUN
UIEB
SUIM
MSU Video Saliency Prediction
Salient-KITTI
Latest papers with no code
Learning Saliency From Fixations
We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps.
Audio-visual Saliency for Omnidirectional Videos
Visual saliency prediction for omnidirectional videos (ODVs) has shown great significance and necessity for omnidirectional videos to help ODV coding, ODV transmission, ODV rendering, etc..
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision
However, these methods ignore the importance of incorporating the explanations of the generated pseudo-labels, or saliency of individual words, as additional guidance during the text classification training process.
UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection
While many approaches have crafted task-specific training paradigms for either video saliency prediction or video salient object detection tasks, few attention has been devoted to devising a generalized saliency modeling framework that seamlessly bridges both these distinct tasks.
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control
To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision.
Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns
To efficiently treat the PSMs of other persons, this paper focuses on the selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons.
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training
We compare the accuracy achieved by our teacher-student training paradigm with (1) training using all available human salience annotations, and (2) using all available training data without human salience annotations.
ViDaS Video Depth-aware Saliency Network
We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos.
MRGAN360: Multi-stage Recurrent Generative Adversarial Network for 360 Degree Image Saliency Prediction
We employ a recurrent neural network among adjacent prediction stages to model their correlations, and exploit a discriminator at the end of each stage to supervise the output saliency map.
CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain.
