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 126–150 of 364 papers

TitleStatusHype
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal FusionCode1
Triple-cooperative Video Shadow DetectionCode1
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
Densely Deformable Efficient Salient Object Detection NetworkCode1
Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration—0
Weakly-Supervised Saliency Detection via Salient Object Subitizing—0
Scene Context-Aware Salient Object DetectionCode1
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance—0
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection—0
Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder—0
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object DetectionCode1
ICNet: Intra-saliency Correlation Network for Co-Saliency DetectionCode1
EF-Net: A novel enhancement and fusion network for RGB-D saliency detectionCode0
AI Marker-based Large-scale AI Literature Mining—0
Fast and Accurate Light Field Saliency Detection through Deep Encoding—0
Learning Selective Mutual Attention and Contrast for RGB-D Saliency DetectionCode1
Light Field Salient Object Detection: A Review and BenchmarkCode1
Hierarchical Domain-Adapted Feature Learning for Video Saliency PredictionCode1
Weakly-supervised Salient Instance Detection—0
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object DetectionCode1
Uncertainty Inspired RGB-D Saliency DetectionCode1
Label Decoupling Framework for Salient Object DetectionCode1
Co-Saliency Detection with Co-Attention Fully Convolutional Network—0
Salient Instance Segmentation with Region and Box-level Annotations—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