Liu, Y., Zhou, L., Wu, G. et al. (2 more authors) (2024) TCGNet: Type-correlation guidance for salient object detection. IEEE Transactions on Intelligent Transportation Systems, 25 (7). pp. 6633-6644. ISSN 1524-9050
Abstract
Contrast and part-whole relations induced by deep neural networks like Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets) have been known as two types of semantic cues for deep salient object detection. However, few works pay attention to their complementary properties in the context of saliency prediction. In this paper, we probe into this issue and propose a Type-Correlation Guidance Network (TCGNet) for salient object detection. Specifically, a Multi-Type Cue Correlation (MTCC) covering CNNs and CapsNets is designed to extract the contrast and part-whole relational semantics, respectively. Using MTCC, two correlation matrices containing complementary information are computed with these two types of semantics. In return, these correlation matrices are used to guide the learning of the above semantics to generate better saliency cues. Besides, a Type Interaction Attention (TIA) is developed to interact semantics from CNNs and CapsNets for the aim of saliency prediction. Experiments and analysis on five benchmarks show the superiority of the proposed approach. Codes has been released on https://github.com/liuyi1989/TCGNet.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Intelligent Transportation Systems is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Salient object detection; part-object relationship; capsule network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Dec 2023 11:51 |
Last Modified: | 04 Oct 2024 16:28 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TITS.2023.3342811 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206631 |