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

Published in

Computer Vision and Pattern Recognition(2020)

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TL;DR

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.

Abstract

Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and inter-image correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three co-saliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.

Authors

Bo Liu

3 papers

Kaihua Zhang

3 papers

Jin Chen

2 papers

References80 items

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs

A Unified Multiple Graph Learning and Convolutional Network Model for Co-saliency Estimation

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

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

Published in

Computer Vision and Pattern Recognition(2020)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Bo Liu

3 papers

Kaihua Zhang

3 papers

Jin Chen

2 papers

References80 items

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs

A Unified Multiple Graph Learning and Convolutional Network Model for Co-saliency Estimation

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

Qingshan Liu

2 papers

Tengpeng Li

2 papers

Shiwen Shen

1 papers

Robust Deep Co-Saliency Detection with Group Semantic

Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing

Attributed Graph Clustering: A Deep Attentional Embedding Approach

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Shifting More Attention to Video Salient Object Detection

Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection

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Image Co-Saliency Detection and Co-Segmentation via Progressive Joint Optimization

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Videos as Space-Time Region Graphs

Iterative Visual Reasoning Beyond Convolutions

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Adaptive Graph Convolutional Neural Networks

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Self-Adaptively Weighted Co-Saliency Detection via Rank Constraint

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Cluster-Based Co-Saliency Detection

Graph Partitioning and Graph Clustering

Object Retrieval Using Visual Query Context

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Field of Study

Computer Science

Journal Information

Name

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Page

11851-11859

Venue Information

Name

Computer Vision and Pattern Recognition

Type

conference

URL

https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147

Alternate Names

CVPR
Comput Vis Pattern Recognit