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 151–175 of 268 papers

TitleStatusHype
SalNet360: Saliency Maps for omni-directional images with CNN—0
SALYPATH: A Deep-Based Architecture for visual attention prediction—0
Scanpath Prediction on Information Visualisations—0
Self-explanatory Deep Salient Object Detection—0
Self-supervised Representation Learning for Ultrasound Video—0
Semantic and Contrast-Aware Saliency—0
Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction—0
Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method—0
Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction—0
SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification—0
SKD: Keypoint Detection for Point Clouds using Saliency Estimation—0
Social Saliency Prediction—0
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency—0
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction—0
Statistically Consistent Saliency Estimation—0
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalizability of Video Saliency—0
SUSiNet: See, Understand and Summarize it—0
Task-driven Webpage Saliency—0
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks—0
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
The Effect of Distortions on the Prediction of Visual Attention—0
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach—0
Training Deep Networks to be Spatially Sensitive—0
Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding—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