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What is Co-Saliency Dataset

Concepts and Techniques of Graph Neural Networks
A collection of multiple images that share common salient objects or regions, used for training and evaluation of co-saliency models.
Published in Chapter:
Study and Analysis of Visual Saliency Applications Using Graph Neural Networks
Gayathri Dhara (SRM University, India) and Ravi Kant Kumar (SRM University, India)
Copyright: © 2023 |Pages: 24
DOI: 10.4018/978-1-6684-6903-3.ch008
Abstract
GNNs (graph neural networks) are deep learning algorithms that operate on graphs. A graph's unique ability to capture structural relationships among data gives insight into more information rather than by analyzing data in isolation. GNNs have numerous applications in different areas, including computer vision. In this chapter, the authors want to investigate the application of graph neural networks (GNNs) to common computer vision problems, specifically on visual saliency, salient object detection, and co-saliency. A thorough overview of numerous visual saliency problems that have been resolved using graph neural networks are studied in this chapter. The different research approaches that used GNN to find saliency and co-saliency between objects are also analyzed.
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