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
Self-supervised co-salient object detection via feature correspondence at multiple scales
Extensive experiments on three CoSOD benchmark datasets show that our self-supervised model outperforms the corresponding state-of-the-art models by a huge margin (e. g. on the CoCA dataset, our model has a 13. 7% F-measure gain over the SOTA unsupervised CoSOD model).
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking
wuyang98/CoSOD
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To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch.
Zero-Shot Co-salient Object Detection Framework
hkxiao/zs-cosod
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Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.
Advancing Referring Expression Segmentation Beyond Single Image
To overcome this limitation, we propose a more realistic and general setting, named Group-wise Referring Expression Segmentation (GRES), which expands RES to a collection of related images, allowing the described objects to be present in a subset of input images.
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
dragonlee258079/DMT
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CVPR 2023
Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Co-Salient Object Detection with Co-Representation Purification
zzy816/corp
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Such irrelevant information in the co-representation interferes with its locating of co-salient objects.
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
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.
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.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
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.
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection
To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects.
