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
Most implemented papers
EGNet: Edge Guidance Network for Salient Object Detection
mindspore-ai/models
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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
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
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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.
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
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.
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
zhengpeng7/mccl
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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
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
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
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
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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
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CVPR 2022
In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.
