SOTAVerified

Saliency Prediction

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.

Papers

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Showing 201–250 of 268 papers

TitleStatusHype
Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video—0
A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)Code0
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks—0
Where's YOUR focus: Personalized Attention—0
Weighted Linear Discriminant Analysis based on Class Saliency Information—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath SimilarityCode0
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalizability of Video Saliency—0
Faster gaze prediction with dense networks and Fisher pruningCode0
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagationCode0
Personalization of Saliency Estimation—0
Saliency Prediction for Mobile User Interfaces—0
Saliency Pattern Detection by Ranking Structured TreesCode0
Understanding Low- and High-Level Contributions to Fixation Prediction—0
Learning Gaze Transitions From Depth to Improve Video Saliency Estimation—0
Dense and Low-Rank Gaussian CRFs Using Deep EmbeddingsCode0
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTMCode0
SalNet360: Saliency Maps for omni-directional images with CNN—0
Human Detection and Tracking for Video Surveillance A Cognitive Science Approach—0
Facial Expression Recognition using Visual Saliency and Deep Learning—0
Self-explanatory Deep Salient Object Detection—0
Training Deep Networks to be Spatially Sensitive—0
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions—0
Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory—0
Predicting Salient Face in Multiple-Face VideosCode0
Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention—0
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection—0
Saliency Revisited: Analysis of Mouse Movements versus Fixations—0
Gaze Distribution Analysis and Saliency Prediction Across Age Groups—0
Deep Visual Attention PredictionCode0
Visual Saliency Prediction Using a Mixture of Deep Neural Networks—0
SalGAN: Visual Saliency Prediction with Generative Adversarial NetworksCode0
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive ModelCode0
Deep, Dense, and Low-Rank Gaussian Conditional Random Fields—0
DeepGaze II: Reading fixations from deep features trained on object recognition—0
Probabilistic Saliency Estimation—0
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited MemoryCode0
A Deep Multi-Level Network for Saliency PredictionCode0
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection—0
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs—0
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction—0
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition—0
WEPSAM: Weakly Pre-Learnt Saliency Model—0
Deep Learning for Saliency Prediction in Natural Video—0
Visual saliency detection: a Kalman filter based approach—0
The Effect of Distortions on the Prediction of Visual Attention—0
Recurrent Mixture Density Network for Spatiotemporal Visual Attention—0
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation—0
Shallow and Deep Convolutional Networks for Saliency PredictionCode0
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks—0
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All datasetsSALECISALICONMIT300CAT2000

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TransalnetKL0.87—Unverified
2TempSALKL0.71—Unverified
3SSwin transformerKL0.65—Unverified
4ECT-SALKL0.58—Unverified
5SUMKL0.47—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC0.88—Unverified
2SalNAS-XL + Self-KDAUC0.87—Unverified
3TempSALAUC0.87—Unverified
4MDS-ViTNetAUC0.87—Unverified
5TranSalNetAUC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC-Judd0.91—Unverified
2TranSalNetAUC-Judd0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMKL0.27—Unverified