Attribute-Guided Feature Learning Network for Vehicle Reidentification

Wang, Huibing; Peng, Jinjia; Chen, Dongyan; Jiang, Guangqi; Zhao, Tongtong; Fu, Xianping

doi:10.1109/mmul.2020.2999464

Cited by 85 publications

(31 citation statements)

References 18 publications

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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%

“…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%

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Vehicle Re-identification in Multi-branches Network

Rong

Zhou

et al. 2021

Preprint

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Vehicle re-identification (Re-ID) aims to solve the problem of matching and identifying the same vehicles under the scene of cross multiple surveillance cameras. Finding the target vehicle quickly and accurately in the massive vehicle database is extremely important for public security, traffic surveillance and applications on smart city. However, it is very challenging due to the orientation variations, illumination changes, occlusion, low resolution, rapid vehicle movement, and amounts of similar vehicle models. In order to overcome these problems and improve the accuracy of vehicle re-identification, a multi-branches network is proposed, which is integrated by global-local feature fusion, channel attention mechanism, and weighted local feature. First, the fusion of global and local features is to obtain more complete information of the vehicle and enhance the learning ability of the model; second, the purpose of embedding the channel attention module in the feature extraction branch is to extract the personalized feature of the vehicle; finally, the influence of sky area and noise information on feature extraction is weakened by weighted local feature. The comprehensive experiments implemented on the mainstream evaluation datasets including VeRi-776, VRIC, and VehicleID indicate that our method can effectively improve the accuracy of vehicle re-identification and is superior to the state-of-the-art methods.

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Section: A Results On Veri-776 Datasetmentioning

confidence: 99%

Section: Vehicle Re-id Methodsmentioning

confidence: 99%

Vehicle Re-identification in Multi-branches Network

Rong

Zhou

et al. 2021

Preprint

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“…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%

An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury

et al. 2021

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Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.

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“…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%

Msav

Wang

Jiang

Peng

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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With the development of multimedia period, information is always caputred with multiple views, which causes a research upsurge on multi-view learning. It is obvious that multi-view data contains more information than those single view ones. Therefore, it is crucial to develop the multi-view algorithms to adapt the demand of many applications. Even though some excellent multi-view algorithms were proposed, most of them can only deal with the specific problems.To tacle this problem, this paper proposes an unified framework named Multi-view Subspace Analysis with View Consistence (MSAV), which provides an unified means to extend those singleview dimension reduciton algorithms into multi-view versions. MSAV first extends multi-view data into kernel space to avoid the problem caused by different dimensions of the data from multiple views. Then, we introduced a self-weighted learning strategy to automatically assign weights for all views according to their importance. Finally, in order to promote the consistence of all views, Hilbert-Schmidt Independence Criterion is adopted by MSAV. Furthermore, We conducted experiments on several benchmark datasets to verify the performance of MSAV. CCS CONCEPTS• Computing methodologies → Feature selection.

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Attribute-Guided Feature Learning Network for Vehicle Reidentification

Cited by 85 publications

References 18 publications

Vehicle Re-identification in Multi-branches Network

Vehicle Re-identification in Multi-branches Network

An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury

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