Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107170
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Qi, Q | en_US |
dc.creator | Zhao, S | en_US |
dc.creator | Shen, J | en_US |
dc.creator | Lam, KM | en_US |
dc.date.accessioned | 2024-06-13T01:04:21Z | - |
dc.date.available | 2024-06-13T01:04:21Z | - |
dc.identifier.isbn | 978-1-5386-9552-4 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5386-9553-1 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107170 | - |
dc.description | 2019 IEEE International Conference on Multimedia and Expo (ICME), 08-12 July 2019, Shanghai, China | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Q. Qi, S. Zhao, J. Shen and K. -M. Lam, "Multi-scale Capsule Attention-Based Salient Object Detection with Multi-crossed Layer Connections," 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019, pp. 1762-1767 is available at https://doi.org/10.1109/ICME.2019.00303. | en_US |
dc.subject | Capsule attention | en_US |
dc.subject | Multi-crossed layer connections | en_US |
dc.subject | Salient object detection | en_US |
dc.title | Multi-scale Capsule attention-based Salient object detection with multi-crossed layer connections | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 1762 | en_US |
dc.identifier.epage | 1767 | en_US |
dc.identifier.doi | 10.1109/ICME.2019.00303 | en_US |
dcterms.abstract | With the popularization of convolutional networks being used for saliency models, saliency detection performance has achieved significant improvement. However, how to integrate accurate and crucial features for modeling saliency is still underexplored. In this paper, we present CapSalNet, which includes a multi-scale Capsule attention module and multi-crossed layer connections for Salient object detection. We first propose a novel capsule attention model, which integrates multi-scale contextual information with dynamic routing. Then, our model adaptively learns to aggregate multi-level features by using multi-crossed skip-layer connections. Finally, the predicted results are efficiently fused to generate the final saliency map in a coarse-to-fine manner. Comprehensive experiments on four benchmark datasets demonstrate that our proposed algorithm outperforms existing state-of-the-art approaches. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2019 IEEE International Conference on Multimedia and Expo (ICME), 08-12 July 2019, Shanghai, China, p. 1762-1767 | en_US |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85071014898 | - |
dc.relation.conference | IEEE International Conference on Multimedia and Expo [ICME] | en_US |
dc.description.validate | 202404 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0343 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20082700 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Lam_Multi-Scale_Capsule_Attention-Based.pdf | Pre-Published version | 1.11 MB | Adobe PDF | View/Open |
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