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 1–10 of 364 papers

TitleStatusHype
Feature Hallucination for Self-supervised Action Recognition—0
Low-Rate Semantic Communication with Codebook-based Conditional Generative Models—0
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization—0
A Deep Learning Framework for Visual Attention Prediction and Analysis of News InterfacesCode0
Copy-Move Detection in Optical Microscopy: A Segmentation Network and A DatasetCode0
Unlocking Comics: The AI4VA Dataset for Visual UnderstandingCode1
AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety—0
VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models—0
AIM 2024 Challenge on Video Saliency Prediction: Methods and ResultsCode1
Evaluating saliency scores in point clouds of natural environments by learning surface anomalies—0
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All datasetsDUT-OMRONHKU-ISCAT2000DUTS-testECSSDPASCAL ContextPASCAL-S

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pyramid Feature AttentionMAE0.04—Unverified
2PFAN [zhao2019pyramid] (+) PRNMAE0.04—Unverified