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 5175 of 364 papers

TitleStatusHype
Learning Adaptive Fusion Bank for Multi-modal Salient Object DetectionCode1
CAGNet: Content-Aware Guidance for Salient Object DetectionCode1
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency DetectionCode1
Learning From Pixel-Level Noisy Label: A New Perspective for Light Field Saliency DetectionCode1
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency DetectionCode1
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic SegmentationCode1
Light Field Saliency Detection with Dual Local Graph Learning andReciprocative GuidanceCode1
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion AttentionCode1
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology ImagesCode1
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object DetectionCode1
Sanity Checks for Saliency MapsCode1
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational AutoencodersCode1
Accurate RGB-D Salient Object Detection via Collaborative LearningCode1
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal FusionCode1
Deeply supervised salient object detection with short connectionsCode1
Finding Visual Saliency in Continuous Spike StreamCode1
ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic VideosCode1
DFNet: Discriminative feature extraction and integration network for salient object detectionCode1
AIM 2024 Challenge on Video Saliency Prediction: Methods and ResultsCode1
Densely Deformable Efficient Salient Object Detection NetworkCode1
Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency DetectionCode1
Few-Cost Salient Object Detection with Adversarial-Paced LearningCode1
A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel ApproachCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Unsupervised Object Localization: Observing the Background to Discover ObjectsCode1
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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