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 201–225 of 364 papers

TitleStatusHype
Evaluating saliency scores in point clouds of natural environments by learning surface anomalies—0
Exemplar-Driven Top-Down Saliency Detection via Deep Association—0
Exploiting Egocentric Object Prior for 3D Saliency Detection—0
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection—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
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
Salient Object Detection via Bootstrap Learning—0
Salient Object Detection via Bounding-box Supervision—0
Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss—0
Self-attention in Vision Transformers Performs Perceptual Grouping, Not Attention—0
Self-Attention Recurrent Network for Saliency Detection—0
Self-explanatory Deep Salient Object Detection—0
SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection—0
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention—0
SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification—0
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings—0
SMCL: Saliency Masked Contrastive Learning for Long-tailed Recognition—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