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 151–200 of 364 papers

TitleStatusHype
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection—0
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection—0
Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model—0
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation—0
A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification—0
A Weighted Sparse Coding Framework for Saliency Detection—0
Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video—0
Bio-inspired visual attention for silicon retinas based on spiking neural networks applied to pattern classification—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
Breast Cancer Detection using Histopathological Images—0
Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning—0
C^4Net: Contextual Compression and Complementary Combination Network for Salient Object Detection—0
Can Image Retrieval help Visual Saliency Detection?—0
Characterness: An Indicator of Text in the Wild—0
Cluster-Based Point Set Saliency—0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery—0
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization—0
Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks—0
Context aware saliency map generation using semantic segmentation—0
Context Proposals for Saliency Detection—0
Contour Loss: Boundary-Aware Learning for Salient Object Segmentation—0
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation—0
Co-Saliency Detection via Looking Deep and Wide—0
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing—0
Co-Saliency Detection with Co-Attention Fully Convolutional Network—0
Co-salient Object Detection Based on Deep Saliency Networks and Seed Propagation over an Integrated Graph—0
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking—0
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection—0
Crowd Saliency Detection via Global Similarity Structure—0
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features—0
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance—0
Deep Learning for Light Field Saliency Detection—0
Deep Learning for Object Saliency Detection and Image Segmentation—0
Deep Level Sets for Salient Object Detection—0
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection—0
Deep Networks for Saliency Detection via Local Estimation and Global Search—0
Deep Reasoning with Multi-Scale Context for Salient Object Detection—0
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection—0
Deep saliency: What is learnt by a deep network about saliency?—0
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective—0
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision—0
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection—0
Contrast-weighted Dictionary Learning Based Saliency Detection for Remote Sensing Images—0
Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling—0
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks—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
Encoding Based Saliency Detection for Videos and Images—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—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