Inter-Domain Adaptation Label for Data Augmentation in Vehicle Re-Identification

Wang, Qi; Min, Weidong; Han, Qing; Liu, Qian; Zha, Cheng; Zhao, Haoyu; Wei, Zitai

doi:10.1109/tmm.2021.3104141

Cited by 32 publications

(11 citation statements)

References 42 publications

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“…In addition, viewpoint-aware network (VANet) 32 is used to learn feature metrics for the same and different viewpoints. Generative adversarial networks (GAN) are used to solve the labeling difficulty in the Re-ID dataset 33 .…”

Section: Related Work On the Vehicle Re-id Taskmentioning

confidence: 99%

A novel dual-pooling attention module for UAV vehicle re-identification

Guo,

Yang,

Jia

et al. 2024

Sci Rep

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Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

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Section: Related Work On the Vehicle Re-id Taskmentioning

confidence: 99%

A novel dual-pooling attention module for UAV vehicle re-identification

Guo,

Yang,

Jia

et al. 2024

Sci Rep

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show abstract

“…General object detection methods, such as SSD [ 6 ], fast RCNN [ 7 ], and faster RCNN [ 8 ], obtained satisfactory results. With the development of deep learning [ 9 , 10 , 11 ] and detection technology, some researchers have attempted to detect important objects. For example, [ 12 , 13 ] studied the importance of generic object categories.…”

Section: Related Researchmentioning

confidence: 99%

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network

Zhao

Min

2021

Sensors

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An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.

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“…Some classical convolutional neural networks (CNNs), such as VGG [ 7 ], Resnet [ 8 ], and DenseUnet [ 9 ], have successfully performed in a variety of computer vision tasks and continue to exhibit breakthroughs in performance. The rapid advancement of CNNs has allowed for the development of a large number of downstream tasks in computer vision to be fully developed [ 10 , 11 , 12 ]. Medical image segmentation has developed at high speed after the application of a fully convolutional network (FCN) [ 13 ] and U-shaped network structure (Unet) [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

et al. 2022

Self Cite

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Due to the complexity of medical imaging techniques and the high heterogeneity of glioma surfaces, image segmentation of human gliomas is one of the most challenging tasks in medical image analysis. Current methods based on convolutional neural networks concentrate on feature extraction while ignoring the correlation between local and global. In this paper, we propose a residual mix transformer fusion net, namely RMTF-Net, for brain tumor segmentation. In the feature encoder, a residual mix transformer encoder including a mix transformer and a residual convolutional neural network (RCNN) is proposed. The mix transformer gives an overlapping patch embedding mechanism to cope with the loss of patch boundary information. Moreover, a parallel fusion strategy based on RCNN is utilized to obtain local–global balanced information. In the feature decoder, a global feature integration (GFI) module is applied, which can enrich the context with the global attention feature. Extensive experiments on brain tumor segmentation from LGG, BraTS2019 and BraTS2020 demonstrated that our proposed RMTF-Net is superior to existing state-of-art methods in subjective visual performance and objective evaluation.

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Inter-Domain Adaptation Label for Data Augmentation in Vehicle Re-Identification

Cited by 32 publications

References 42 publications

A novel dual-pooling attention module for UAV vehicle re-identification

A novel dual-pooling attention module for UAV vehicle re-identification

A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

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