2020
DOI: 10.1109/mmul.2020.2999464
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Attribute-Guided Feature Learning Network for Vehicle Reidentification

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Cited by 95 publications
(36 citation statements)
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“…These results surpass previous state-of-the-art on almost the three metrics, especially on mAP. In this paper, our method only relies on the supervised information of ID, while VGG+C+T [32], GS-TRE [33], VAMI+ST [34], and AGNet-ASL+STR [15] exploit spatial-temporal information, and other methods also utilize extra annotations such as attributes (RAM [6]), but our accuracy still exceeds them. In a word, re-ranking can further enhance the performance of our model.…”
Section: A Results On Veri-776 Datasetmentioning
confidence: 99%
See 1 more Smart Citation
“…These results surpass previous state-of-the-art on almost the three metrics, especially on mAP. In this paper, our method only relies on the supervised information of ID, while VGG+C+T [32], GS-TRE [33], VAMI+ST [34], and AGNet-ASL+STR [15] exploit spatial-temporal information, and other methods also utilize extra annotations such as attributes (RAM [6]), but our accuracy still exceeds them. In a word, re-ranking can further enhance the performance of our model.…”
Section: A Results On Veri-776 Datasetmentioning
confidence: 99%
“…Zheng et al [14] proposed a multiscale attention framework (MSA) to fusing the discriminative local cues and effective global information. Wang et al [15] designed AGNet with attribute-guided attention module which could learn global representation with abundant attribute features in an end-to-end manner. He et al [16] used a simple and efficient part-regularized discriminative feature preserving method, which improves the recognition ability of subtle information.…”
Section: Vehicle Re-id Methodsmentioning
confidence: 99%
“…Convolutional Neural Networks (CNNs) can produce outstanding performance in performing various tasks related to computer vision such as vehicle recognition [ 27 , 28 ], image generation [ 29 , 30 ], and the segmentation of automatic hemorrhagic lesion on CT scans. Farzaneh et al [ 31 ] proposed an approach to SDH segmentation for TBI using a conventional feature extraction algorithm and a TreeBagger classifier.…”
Section: Related Workmentioning
confidence: 99%
“…Therefore, how to construct an unified framework which can extend various single view algorithm to the corresponding mutli-view version is crucial for the field of multi-view learning. In this paper, we focus on constructing an unified framework for the problem of multi-view subspace analysis, which benefits the fields of computer vision [18] and machine learning.…”
Section: Introductionmentioning
confidence: 99%