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http://hdl.handle.net/10397/105694
DC Field | Value | Language |
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dc.contributor | Department of Computing | - |
dc.creator | Wang, K | en_US |
dc.creator | Lin, L | en_US |
dc.creator | Zuo, W | en_US |
dc.creator | Gu, S | en_US |
dc.creator | Zhang, L | en_US |
dc.date.accessioned | 2024-04-15T07:35:56Z | - |
dc.date.available | 2024-04-15T07:35:56Z | - |
dc.identifier.isbn | 978-1-4673-8851-1 (Electronic) | en_US |
dc.identifier.isbn | 978-1-4673-8852-8 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105694 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2016 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 K. Wang, L. Lin, W. Zuo, S. Gu and L. Zhang, "Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2138-2146 is available at https://doi.org/10.1109/CVPR.2016.235. | en_US |
dc.title | Dictionary pair classifier driven convolutional neural networks for object detection | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 2138 | en_US |
dc.identifier.epage | 2146 | en_US |
dc.identifier.doi | 10.1109/CVPR.2016.235 | en_US |
dcterms.abstract | Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for the final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN [12]), and achieves new state-of-the-arts. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 June - 1 July 2016, Las Vegas, Nevada, p. 2138-2146 | en_US |
dcterms.issued | 2016 | - |
dc.identifier.scopus | 2-s2.0-84986327418 | - |
dc.relation.conference | IEEE Conference on Computer Vision and Pattern Recognition [CVPR] | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1383 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polyutechnic University’s Joint Supervision Scheme with the Chinese Mainland, Taiwan and Macao Universities; Guangdong Natural Science Foundation; Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund; Fundamental Research Funds for the Central Universities | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 13932353 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Gu_Dictionary_Pair_Classifier.pdf | Pre-Published version | 1.76 MB | Adobe PDF | View/Open |
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