Enhancing Part Features via Contrastive Attention Module for Vehicle Re-identification

Li, Manyu; Wei, Mengwan; He, Xin; Shen, Fei

doi:10.1109/icip46576.2022.9897943

Cited by 18 publications

(15 citation statements)

References 26 publications

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“…Recently, attention mechanism methods [26][27][28][29][30] have been introduced to semantic segmentation. Transformer mechanisms and self-attention mechanisms [31] were initially introduced in the field of natural language processing and later sparked widespread research interest in the field of computer vision [32][33][34][35].…”

Section: Semantic Segmentationmentioning

confidence: 99%

SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Song,

He,

Dai

et al. 2024

Preprint

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Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

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Section: Semantic Segmentationmentioning

confidence: 99%

SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Song,

He,

Dai

et al. 2024

Preprint

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“…The network captures the global structural information of the vehicle through a global relational attention module to improve the accuracy of re-ID. Li et al [117] investigated a CAM network with a contrast attention module. It enhanced the recognition ability of the re-ID model by refining the local features.…”

Section: Vehicle Re-identification Based On Attention Mechanismmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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“…The extraction of contextual information is widely utilized across various domains [13][14][15][16] within artificial intelligence. For example, Deeplab-v3 [17] utilizes atrous convolution which magnifies the receptive field to acquire muti-scale context while decreasing the loss of information.…”

Section: B Context Exploitationmentioning

confidence: 99%

Preface: Celebrating the 100th anniversary of chemical sciences in Nanjing University

Wang

2020

Sci. China Chem.

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The generalized weak Galerkin (gWG) finite element method is proposed and analyzed for the biharmonic equation. A new generalized discrete weak second order partial derivative is introduced in the gWG scheme to allow arbitrary combinations of piecewise polynomial functions defined in the interior and on the boundary of general polygonal or polyhedral elements. The error estimates are established for the numerical approximation in a discrete H 2 norm and a L 2 norm. The numerical results are reported to demonstrate the accuracy and flexibility of our proposed gWG method for the biharmonic equation.

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Enhancing Part Features via Contrastive Attention Module for Vehicle Re-identification

Cited by 18 publications

References 26 publications

SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Preface: Celebrating the 100th anniversary of chemical sciences in Nanjing University

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