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: 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.
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.
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
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
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
In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process.
