SOTAVerified

Saliency Detection

Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.

Source: An Unsupervised Game-Theoretic Approach to Saliency Detection

Papers

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Showing 226–250 of 364 papers

TitleStatusHype
Spectrum-inspired Low-light Image Translation for Saliency Detection—0
SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Generation—0
Statistical Textural Distinctiveness for Salient Region Detection in Natural Images—0
Submodular Salient Region Detection—0
Supervised Adversarial Networks for Image Saliency Detection—0
Synergistic saliency and depth prediction for RGB-D saliency detection—0
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography—0
Task-driven Webpage Saliency—0
Temporal-Spatial Feature Pyramid for Video Saliency Detection—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
Top-Down Saliency Detection Driven by Visual Classification—0
Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration—0
Towards Effective Codebookless Model for Image Classification—0
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context—0
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models—0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Towards Mesh Saliency Detection in 6 Degrees of Freedom—0
Towards Stable Co-saliency Detection and Object Co-segmentation—0
Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder—0
Transformer-based Network for RGB-D Saliency Detection—0
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization—0
Unsupervised learning of foreground object detection—0
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism—0
Unsupervised Neural Architecture for Saliency Detection: Extended Version—0
Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection—0
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All datasetsDUT-OMRONHKU-ISCAT2000DUTS-testECSSDPASCAL ContextPASCAL-S

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UCFMAE0.12—Unverified
2U2-Net+MAE0.06—Unverified
3U2-NetMAE0.05—Unverified
4LDF(ours)MAE0.05—Unverified
5Pyramid Feature AttentionMAE0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1U2-Net+MAE0.04—Unverified
2Pyramid Feature AttentionMAE0.03—Unverified
3LDF(ours)MAE0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC0.89—Unverified
2EYMOLAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.04—Unverified
2PFAN [zhao2019pyramid] (+) PRNMAE0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1InvPTmax_F184.81—Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.07—Unverified