Co-Salient Object Detection
21 papers with code • 4 benchmarks • 2 datasets
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 )
CoSal2015
CAT: Context Adjustment Training
Latest papers with no code
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection
In this paper, we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations, which are subsequently fed to CoSODs for robustness enhancement.
Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detection
Co-Salient Object Detection (CoSOD) is a rapidly growing task, extended from Salient Object Detection (SOD) and Common Object Segmentation (Co-Segmentation).
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
Our unsupervised model is a great pre-training initialization for our semi-supervised model SS-CoSOD, especially when very limited labeled data is available for training.
Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion
Given a group of images, co-salient object detection (CoSOD) aims to highlight the common salient object in each image.
SegGPT Meets Co-Saliency Scene
Co-salient object detection targets at detecting co-existed salient objects among a group of images.
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking
To address this issue, this paper presents a group exchange-masking (GEM) strategy for robust CoSOD model learning.
Generalised Co-Salient Object Detection
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object.
CoSformer: Detecting Co-Salient Object with Transformers
Co-Salient Object Detection (CoSOD) aims at simulating the human visual system to discover the common and salient objects from a group of relevant images.
Co-Saliency Detection with Co-Attention Fully Convolutional Network
Co-saliency detection aims to detect common salient objects from a group of relevant images.
An End-to-End Network for Co-Saliency Detection in One Single Image
Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed.
