Saliency Detection
130 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
Latest papers
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Pyramidal Attention for Saliency Detection
tanveer-hussain/efficientsod2
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Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance.
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
Weakly-Supervised Salient Object Detection Using Point Supervision
shuyonggao/psod
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Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps.
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction.
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
suyukun666/UFO
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Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images.
3SD: Self-Supervised Saliency Detection With No Labels
Our method generates and uses pseudo-ground truth labels for training.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
YangtaoWANG95/TokenCut
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CVPR 2022
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback
hankebo/spvp360
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Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance.
