Saliency Detection
129 papers with code • 7 benchmarks • 13 datasets
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
Benchmarks
These leaderboards are used to track progress in Saliency Detection
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Libraries
Use these libraries to find Saliency Detection models and implementations
PASCAL Context
PASCAL-S
DUTS
HKU-IS
DUT-OMRON
iSUN
CAT2000
ECSSD
ReDWeb-S
Lytro Illum
Subtasks
Most implemented papers
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
rmcong/Hybrid-Label-SOD_TCSVT2022
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In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Inner and Inter Label Propagation: Salient Object Detection in the Wild
For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme.
Deep Saliency with Encoded Low level Distance Map and High Level Features
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
Visual Saliency Detection Based on Multiscale Deep CNN Features
vlad-winter/FakePapers_NLP_Project
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The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature.
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
Furthermore, the proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.
Non-Local Deep Features for Salient Object Detection
Saliency detection aims to highlight the most relevant objects in an image.
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
One is the lack of tremendous amount of annotated data to train a network.
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Many works have been done on salient object detection using supervised or unsupervised approaches on colour images.
