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

Showing 301325 of 364 papers

TitleStatusHype
Saliency Detection via Graph-Based Manifold Ranking0
Saliency Detection With Fully Convolutional Neural Network0
Saliency detection with moving camera via background model completion0
Saliency Detection with Spaces of Background-based Distribution0
Saliency-Driven Versatile Video Coding for Neural Object Detection0
Saliency Guided Dictionary Learning for Weakly-Supervised Image Parsing0
Personalized Saliency and its PredictionCode0
PiCANet: Learning Pixel-wise Contextual Attention for Saliency DetectionCode0
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency DetectionCode0
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency DetectionCode0
Copy-Move Detection in Optical Microscopy: A Segmentation Network and A DatasetCode0
TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency DetectionCode0
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object DetectionCode0
Non-Local Deep Features for Salient Object DetectionCode0
Multi-source weak supervision for saliency detectionCode0
Motion Guided Attention for Video Salient Object DetectionCode0
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale FeaturesCode0
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB ImagesCode0
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative StudyCode0
A Stagewise Refinement Model for Detecting Salient Objects in ImagesCode0
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object SegmentationCode0
Memory-oriented Decoder for Light Field Salient Object DetectionCode0
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency DetectionCode0
Salient object detection on hyperspectral images using features learned from unsupervised segmentation taskCode0
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UCFMAE0.12Unverified
2U2-Net+MAE0.06Unverified
3U2-NetMAE0.05Unverified
4LDF(ours)MAE0.05Unverified
5Pyramid Feature AttentionMAE0.04Unverified
#ModelMetricClaimedVerifiedStatus
1U2-Net+MAE0.04Unverified
2Pyramid Feature AttentionMAE0.03Unverified
3LDF(ours)MAE0.03Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC0.89Unverified
2EYMOLAUC0.83Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.04Unverified
2PFAN [zhao2019pyramid] (+) PRNMAE0.04Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.03Unverified
#ModelMetricClaimedVerifiedStatus
1InvPTmax_F184.81Unverified
#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.07Unverified