PoolNet+: Exploring the Potential of Pooling for Salient Object Detection

We explore the potential of pooling techniques on the task of salient object detection by expanding its role in convolutional neural networks. In general, two pooling-based modules are proposed. A global guidance module (GGM) is first built based on the bottom-up pathway of the U-shape architecture,...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 1; pp. 887 - 904
Main Authors:	Liu, Jiang-Jiang, Hou, Qibin, Liu, Zhi-Ang, Cheng, Ming-Ming
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Applications programs Artificial neural networks Convolutional neural networks Corresponding states Edge detection feature aggregation Feature extraction global guidance Image edge detection Image segmentation mobile application Mobile computing Modules Object detection Object recognition pooling techniques Salience Salient object detection Semantics Task analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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