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Co-Salient Object Detection

Co-Salient Object Detection is a computational problem that aims at highlighting the common and salient foreground regions (or objects) in an image group. Please also refer to the online benchmark: http://dpfan.net/cosod3k/

( Image credit: Taking a Deeper Look at Co-Salient Object Detection, CVPR2020 )

Papers

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Showing 26–44 of 44 papers

TitleStatusHype
Review of Visual Saliency Detection with Comprehensive Information—0
SegGPT Meets Co-Saliency Scene—0
An Iterative Co-Saliency Framework for RGBD Images—0
Generalised Co-Salient Object Detection—0
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics—0
Group-wise Deep Co-saliency Detection—0
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images—0
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization—0
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection—0
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges—0
An End-to-End Network for Co-Saliency Detection in One Single Image—0
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection—0
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation—0
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency DetectionCode0
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-MaskingCode0
Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object DetectionCode0
Global-and-Local Collaborative Learning for Co-Salient Object DetectionCode0
EGNet: Edge Guidance Network for Salient Object DetectionCode0
Self-supervised co-salient object detection via feature correspondence at multiple scalesCode0
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