ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification

Ren, Huimin; Pan, Menghai; Li, Yanhua; Zhou, Xun; Luo, Jun

doi:10.1145/3394486.3403183

Cited by 28 publications

(9 citation statements)

References 24 publications

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“…In recent years, scene text recognition has achieved tremendous progress owing to the rapid development of deep learning. It has been widely used in many real-world applications such as auto-driving (Zhang et al 2020a), ID card recognition (Satyawan et al 2019), signature identification (Ren et al 2020), etc. Although the recently proposed recognizers become stronger as reported, we observe that low-resolution (LR) text images still pose great challenges for them.…”

Section: Introductionmentioning

confidence: 99%

Text Gestalt: Stroke-Aware Scene Text Image Super-resolution

Chen

et al. 2022

AAAI

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In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.

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Section: Introductionmentioning

confidence: 99%

Text Gestalt: Stroke-Aware Scene Text Image Super-resolution

Chen

et al. 2022

AAAI

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“…Inspired by the work of the Inception model [30] and others [33], [34], this paper attempts to take target characteristics from different scales. The Inception model first attempts multibranch convolution with different kernel sizes, which extends the convolution operation between layers of the neural network, resulting in different sizes of perceptual fields.…”

Section: A Multiscale Feature Extractionmentioning

confidence: 99%

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Wang

Liu

Cui

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Feature selection is an important way to achieve high precision and efficient classification of hyperspectral remote sensing images. However, most existing FS methods use a fixed scale to extract features and the relationship between spatial and spectral dimensions is ignored. In fact, this correlation is useful for classification. In this paper, a multi-scale feature fusion network (MSFGW) is proposed in which a global weighting mechanism is explored to catch spatial-spectral information at multiple scales. First, the multi-scale feature extraction module composed of group convolution and dilated convolution is utilized to extract the multiscale features. With the increase of the dilation rate, the module takes the spatial differences at varying scales. Secondly, a 3D weighting mechanism is used to combine the spatial and spectral correlated information for reducing the interference of homologous and heterologous and boosting the feature discrimination ability. Then, multi-scale weighted features are fused to integrate the internal information of all bands at different scales. Finally, the band reconstruction network is used to select representative bands according to their entropy. The experimental results with the state-of-the-art feature selection algorithms on three widely hyperspectral datasets demonstrate that the features selected by MSFGW have obvious advantages in classification with only a few training samples.

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“…Kieu et al [9] consider trajectory data as an image which is feed to convolutional neural network to learn embeddings to represent trajectories. Ren et al [13] extract manually defined patterns from data and employ the siamese networks to them which is capable train a metric to measure the similarity between historical trajectories. This line of research is limited to the physical environment variables.…”

Section: Representation Learning In Trajectorymentioning

confidence: 99%

DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation

Wang¹,

Zhou²,

Zhou³

et al. 2022

Preprint

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Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches tend to rarely address three challenges: 1) how to represent the intricate semantic intentions of mobility inexpensively; 2) complex and weak spatial-temporal dependencies due to the sparsity and heterogeneity of the trajectory data; 3) route selection preferences and their correlation to driving behavior. In this paper, we propose a novel multimodal fusion model, DouFu, for trajectory representation joint learning, which applies multimodal learning and attention fusion module to capture the internal characteristics of trajectories. We first design movement, route, and global features generated from the trajectory data and urban functional zones and then analyze them respectively with the attention encoder or feed forward network. The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding. With the global semantic feature, DouFu produces a comprehensive embedding for each trajectory. We evaluate representations generated by our method and other baseline models on classification and clustering tasks. Empirical results show that DouFu outperforms other models in most of the learning algorithms like the linear regression and the support vector machine by more than 10%.

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ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification

Cited by 28 publications

References 24 publications

Text Gestalt: Stroke-Aware Scene Text Image Super-resolution

Text Gestalt: Stroke-Aware Scene Text Image Super-resolution

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation

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