Co-Salient Object Detection

18 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:

( 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.

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.

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.

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

rmcong/CoADNet_NeurIPS20 NeurIPS 2020

In the first stage, we propose a group-attentional semantic aggregation module that models inter-image relationships to generate the group-wise semantic representations.

Group Collaborative Learning for Co-Salient Object Detection

fanq15/GCoNet CVPR 2021

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.

Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection

nnizhang/cadc ICCV 2021

In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process.