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 251–275 of 364 papers

TitleStatusHype
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
Saliency Detection via Graph-Based Manifold Ranking—0
Saliency Detection With Fully Convolutional Neural Network—0
Saliency detection with moving camera via background model completion—0
Saliency Detection with Spaces of Background-based Distribution—0
Saliency-Driven Versatile Video Coding for Neural Object Detection—0
Saliency Guided Dictionary Learning for Weakly-Supervised Image Parsing—0
Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition—0
Saliency map using features derived from spiking neural networks of primate visual cortex—0
Saliency Propagation From Simple to Difficult—0
Salient Object Detection by Lossless Feature Reflection—0
Salient Object Detection in Video using Deep Non-Local Neural Networks—0
Where's YOUR focus: Personalized Attention—0
Salient Object Detection via Bootstrap Learning—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