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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 364 papers

TitleStatusHype
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation—0
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff—0
Review of Visual Saliency Detection with Comprehensive Information—0
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning—0
RGBD Salient Object Detection via Deep Fusion—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
Robust Saliency Detection via Fusing Foreground and Background Priors—0
Robust Saliency Detection via Regularized Random Walks Ranking—0
SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection—0
Arbitrary Handwriting Image Style Transfer—0
HDR image watermarking using saliency detection and quantization index modulation—0
Saliency detection by aggregating complementary background template with optimization framework—0
Saliency Detection by Multi-Context Deep Learning—0
Saliency Detection combining Multi-layer Integration algorithm with background prior and energy function—0
Saliency Detection for Improving Object Proposals—0
Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons—0
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion—0
Saliency Detection in 360° Videos—0
Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions—0
Saliency Detection on Light Field—0
Saliency Detection via Bidirectional Absorbing Markov Chain—0
Saliency Detection via Cellular Automata—0
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs—0
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss—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