A Large-Scale Benchmark for Vehicle Logo Recognition

Liu, Jiajun; Shen, Fei; Wei, Mengwan; Zhang, Yuzhao; Zeng, Huanqiang; Zhu, Jianqing; Cai, Canhui

doi:10.1109/icivc47709.2019.8981041

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…In visual tasks involving natural images [49][50][51][52], remote sensing images [15,17,53,54], and visible light images [55][56][57][58][59], a substantial body of work has sought to address the subjectivity issue in balancing this trade-off by learning to integrate multi-scale information. Specifically, these approaches learn representations from multiple parallel networks and then aggregate information across different scales before making the final predictions.…”

Section: Dual-branch Architecturementioning

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: Dual-branch Architecturementioning

confidence: 99%

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

Song,

He,

Dai

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, there are also methods [31,32] that enhance both the feature extraction ability of the model at the encoder side and the feature fusion ability at the decoder side. Yang et al [33] incorporated a dense connection and multi-scale maximum pool module at the encoder end, while adding the ECA attention mechanism module to the decoder for simultaneous feature mapping from different coding layers.…”

Section: Remote Sensing Image Semantic Segmentationmentioning

confidence: 99%

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

Zhang,

Xie

2023

Preprint

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The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

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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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A Large-Scale Benchmark for Vehicle Logo Recognition

Cited by 10 publications

References 14 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

HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation

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

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