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 151–175 of 268 papers

TitleStatusHype
Learning to Explore Intrinsic Saliency for Stereoscopic Video—0
An Iterative and Cooperative Top-Down and Bottom-Up Inference Network for Salient Object Detection—0
DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency PredictionCode0
How is Gaze Influenced by Image Transformations? Dataset and ModelCode1
Salient Object Detection in the Deep Learning Era: An In-Depth SurveyCode0
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning—0
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency—0
A Dilated Inception Network for Visual Saliency PredictionCode0
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks—0
Multi-source weak supervision for saliency detectionCode0
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object DetectionCode0
Fast Underwater Image Enhancement for Improved Visual PerceptionCode0
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention—0
Understanding and Visualizing Deep Visual Saliency ModelsCode0
Contextual Encoder-Decoder Network for Visual Saliency PredictionCode0
Deep Reasoning with Multi-Scale Context for Salient Object Detection—0
Salient Object Detection via High-to-Low Hierarchical Context Aggregation—0
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency DetectionCode0
SUSiNet: See, Understand and Summarize it—0
A Neurodynamic model of Saliency prediction in V1Code0
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction—0
Visual Saliency Maps Can Apply to Facial Expression Recognition—0
Semantic and Contrast-Aware Saliency—0
Bottom-up Attention, Models of—0
Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges—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