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 326–350 of 364 papers

TitleStatusHype
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision—0
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection—0
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection—0
Towards Mesh Saliency Detection in 6 Degrees of Freedom—0
Towards Stable Co-saliency Detection and Object Co-segmentation—0
Encoding Based Saliency Detection for Videos and Images—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—0
Evaluating saliency scores in point clouds of natural environments by learning surface anomalies—0
Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder—0
Exemplar-Driven Top-Down Saliency Detection via Deep Association—0
Transformer-based Network for RGB-D Saliency Detection—0
Exploiting Egocentric Object Prior for 3D Saliency Detection—0
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection—0
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision—0
Extracting Human Attention through Crowdsourced Patch Labeling—0
Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network—0
Fast and Accurate Light Field Saliency Detection through Deep Encoding—0
Fast Hybrid Image Retargeting—0
Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms—0
Feature Hallucination for Self-supervised Action Recognition—0
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective—0
Deep saliency: What is learnt by a deep network about saliency?—0
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection—0
Free Lunch for Co-Saliency Detection: Context Adjustment—0
Fristograms: Revealing and Exploiting Light Field Internals—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