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computer-vision-7

Co-Salient Object Detection

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in co-salient-object-detection-21

Trend

Dataset

Best Model

Actions

CoSOD3k

CoSOD3k

CoSal2015

CoSal2015

CoCA

CoCA

Libraries

i

Use these libraries to find co-salient-object-detection-21 models and implementations

Datasets

CoSal2015

CAT: Context Adjustment Training

CAT: Context Adjustment Training

Subtasks

No subtasks available.

Most implemented papers

EGNet: Edge Guidance Network for Salient Object Detection

Jiangjiang Liu, Ming-Ming Cheng, Deng-Ping Fan, Jufeng Yang, Jiaxing Zhao, Yang Cao•Wed Aug 21 2019

This paper presents an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network to solve the complementarity between salient edge information and salient object information.

1031

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

iCoSeg

iCoSeg

0

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

Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang•Fri May 31 2019

In this paper, we address a new task called instance co-segmentation. Given a set of images jointly covering object instances of a specific category, instance co-segmentation aims to identify all of these instances and segment each of them, i.e. generating one mask for each instance. This task is important since instance-level segmentation is preferable for humans and many vision applications. It is also challenging because no pixel-wise annotated training data are available and the number of instances in each image is unknown. We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation. In the former sub-task, we develop a CNN-based network to detect the co-peaks as well as co-saliency maps for a pair of images. A co-peak has two endpoints, one in each image, that are local maxima in the response maps and similar to each other. Thereby, the two endpoints are potentially covered by a pair of instances of the same category. In the latter subtask, we design a ranking function that takes the detected co-peaks and co-saliency maps as inputs and can select the object proposals to produce the final results. Our method for instance co-segmentation and its variant for object colocalization are evaluated on four datasets, and achieve favorable performance against the state-of-the-art methods. The source codes and the collected datasets are available at https://github.com/KuangJuiHsu/DeepCO3/

65 0

Re-Thinking Co-Salient Object Detection

Jianbing Shen, Ming-Ming Cheng, Deng-Ping Fan, Dingwen Zhang, H. Fu, Ge-Peng Ji, Zheng Lin, Tengpeng Li•Mon Jul 06 2020

A novel CoEG-Net is proposed that augments the authors' prior model EGNet with a co-attention projection strategy to enable fast common information learning and fully leverages previous large-scale SOD datasets and significantly improves the model scalability and stability.

164 0

Gradient-Induced Co-Saliency Detection

Ming-Ming Cheng, Zhao Zhang, Jun Xu, Wenda Jin•Mon Apr 27 2020

A gradient-induced co-saliency detection (GICD) method that first abstracts a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, utilizes the feedback gradient information to induce more attention to the discriminative co- salient features.

130 0

Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection

Bo Liu, Kaihua Zhang, Jin Chen, Qingshan Liu, Tengpeng Li, Shiwen Shen•Thu Mar 12 2020

This work presents a novel adaptive graph convolutional network with attention graph clustering (GCAGC), and proposes a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutionsal network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner.

105 0

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

Qijian Zhang, Runmin Cong, Chongyi Li, Yao Zhao, Junhui Hou•Mon Nov 09 2020

This paper presents an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images, and develops a group consistency preserving decoder tailored for the CoSOD task.

68 0

Taking a Deeper Look at Co-Salient Object Detection

Ming-Ming Cheng, Deng-Ping Fan, Dingwen Zhang, H. Fu, Ge-Peng Ji, Zheng Lin•Sun May 31 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. However, existing CoSOD datasets often have a serious data bias, which assumes that each group of images contains salient objects of similar visual appearances. This bias results in the ideal settings and the effectiveness of the models, trained on existing datasets, may be impaired in real-life situations, where the similarity is usually semantic or conceptual. To tackle this issue, we first collect a new high-quality dataset, named CoSOD3k, which contains 3,316 images divided into 160 groups with multiple level annotations, i.e., category, bounding box, object, and instance levels. CoSOD3k makes a significant leap in terms of diversity, difficulty and scalability, benefiting related vision tasks. Besides, we comprehensively summarize 34 cutting-edge algorithms, benchmarking 19 of them over four existing CoSOD datasets (MSRC, iCoSeg, Image Pair and CoSal2015) and our CoSOD3k with a total of ∼61K images (largest scale), and reporting group-level performance analysis. Finally, we discuss the challenge and future work of CoSOD. Our study would give a strong boost to growth in the CoSOD community. Benchmark toolbox and results are available on our project page.

87 0

Group Collaborative Learning for Co-Salient Object Detection

Ling Shao, Deng-Ping Fan, Yu-Wing Tai, H. Fu, Chi-Keung Tang, Qi Fan•Sun Mar 14 2021

This paper’s new technical contributions on a number of important downstream computer vision applications including content aware co-segmentation, co-localization based automatic thumbnails, etc are demonstrated.

97 0

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

Qing Guo, Wei Feng, H. Fu, Hongkai Yu, Song Wang, Ruijun Gao, Felix Juefei-Xu, Yang Liu•Fri Sep 18 2020

The very first blackbox joint adversarial exposure and noise attack (Jadena), where they jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function, leads to significant performance degradation on various co- saliency detection datasets and makes the co-salient objects undetectable.

18 0

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

Ling Shao, Junwei Han, Nian Liu, Ni Zhang•Thu Sep 30 2021

A novel consensus-aware dynamic convolution model is proposed to explicitly and effectively perform the "summarize and search" process to summarize consensus image features and generate dynamic kernels from consensus features to encode the summarized consensus knowledge.

64 0

Adding a benchmark result helps the community track progress.

Co-Salient Object Detection | State-of-the-Art