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 201–225 of 268 papers

TitleStatusHype
Personalization of Saliency Estimation—0
Probabilistic Saliency Estimation—0
RecSal : Deep Recursive Supervision for Visual Saliency Prediction—0
Recurrent Mixture Density Network for Spatiotemporal Visual Attention—0
Reinforced Abstractive Summarization with Adaptive Length Controlling—0
Relevance-guided Audio Visual Fusion for Video Saliency Prediction—0
Rethinking gradient weights' influence over saliency map estimation—0
SalFoM: Dynamic Saliency Prediction with Video Foundation Models—0
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks—0
How is Visual Attention Influenced by Text Guidance? Database and ModelCode0
How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and ModelCode0
Understanding and Visualizing Deep Visual Saliency ModelsCode0
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1Code0
Modeling Object Dissimilarity for Deep Saliency PredictionCode0
Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D GraphicsCode0
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human ParsingCode0
Multi-source weak supervision for saliency detectionCode0
Gravitational Laws of Focus of AttentionCode0
Noise-Aware Video Saliency PredictionCode0
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath SimilarityCode0
Fast Underwater Image Enhancement for Improved Visual PerceptionCode0
Faster gaze prediction with dense networks and Fisher pruningCode0
Bridging the Gap Between Saliency Prediction and Image Quality AssessmentCode0
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object DetectionCode0
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagationCode0
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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