Co-Salient Object Detection

22 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 )

Most implemented papers

EGNet: Edge Guidance Network for Salient Object Detection

mindspore-ai/models ICCV 2019

In the second step, we integrate the local edge information and global location information to obtain the salient edge features.

GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector

zhengpeng7/gconet_plus 30 May 2022

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.

Re-thinking Co-Salient Object Detection

DengPingFan/CoEGNet 7 Jul 2020

CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.

Global-and-Local Collaborative Learning for Co-Salient Object Detection

HarveyYeung/GLNet-TCYB2022-MindSpore.git 19 Apr 2022

In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

zhengpeng7/mccl 28 Feb 2023

To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories.

DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection

KuangJuiHsu/DeepCO3 CVPR 2019

We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation.

Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

ltp1995/GCAGC-CVPR2020 CVPR 2020

Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion.

Gradient-Induced Co-Saliency Detection

zzhanghub/gicd ECCV 2020

Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images.

Taking a Deeper Look at Co-Salient Object Detection

DengPingFan/CoEGNet CVPR 2020

Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of salient object detection (SOD), which aims to detect the co-occurring salient objects in multiple images.

Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection

tsingqguo/jadena CVPR 2022

In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.