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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 268 papers

TitleStatusHype
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
Show:102550
← PrevPage 10 of 11Next →
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