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Journal articles on the topic 'Siamese Tales'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Waters, Tony. "The “Consulting Detective” Nithan Thong-in by King Vajiravudh." Journal of the Siam Society 112, no. 1 (2024): 191–96. http://dx.doi.org/10.69486/112.1.2024.10.

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This short study explores detective stories by Crown Prince Vajiravudh, later King Rama VI, in early 20th-century Siam. Compiled in Nithan Thong-in, these tales provide insights into Siamese society, culture, and history. Despite multiple reprints, an English translation is currently unavailable. This notice introduces the cultural richness of Mr Thong-in’s adventures, along with brief summaries of each story.

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2

Wilujeng, Catherine Wahyuning, and Azahra Andriani. "Strengthening and Facilitating Community Groups of The Dewata Orchid Program to Address Agribusiness Issues of Siamese Tangerines in Air Talas Village, Muara Enim Regency, South Sumatra." International Journal of Social Science and Community Service 2, no. 3 (2024): 210–15. https://doi.org/10.70865/ijsscs.v2i3.39.

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Since the transmigration program to Air Talas Village, Muara Enim Regency, in 1987, the majority of the community has been cultivating Siamese tangerines (Citrus tangerina). As a result, this village is known as a producer of Siamese tangerines, with an annual production exceeding 300 tons. However, this high production has yet to be matched with good sales, leading to an oversupply and a decline in selling prices. The presence of Citrus Vein Phloem Degeneration (CVPD) disease poses a threat to tangerine production as it can degrade the quality of the fruit. This situation can reduce the incom

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3

Xu, Yuxin, Yuyao Yan, Yiming Lin, Xi Yang, and Kaizhu Huang. "Sketch Based Image Retrieval for Architecture Images with Siamese Swin Transformer." Journal of Physics: Conference Series 2278, no. 1 (2022): 012035. http://dx.doi.org/10.1088/1742-6596/2278/1/012035.

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Abstract Sketch-based image retrieval (SBIR) is an image retrieval task that takes a sketch as input and outputs colour images matching the sketch. Most recent SBIR methods utilise deep learning methods with complicated network designs, which are resource-intensive for practical use. This paper proposes a novel compact framework that takes the siamese network with image view angle information, targeting the SBIR task for architecture images. In particular, the proposed siamese network engages a compact SwinTiny transformer as the backbone encoder. View angle information of the architecture ima

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Wang, Yu, Shuyang Ma, and Xuanjing Shen. "A Novel Video Face Verification Algorithm Based on TPLBP and the 3D Siamese-CNN." Electronics 8, no. 12 (2019): 1544. http://dx.doi.org/10.3390/electronics8121544.

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In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-patch local binary pattern (TPLBP) and the 3D Siamese convolutional neural network is proposed in this paper. The proposed method takes the TPLBP texture feature which has excellent performance in face analysis as the input of the network. In order to extract the inter-frame information of the video, the texture feature maps of the multi-frames are st

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Russel, Newlin Shebiah, S. Arivazhagan, S. G. Amrith, and S. Adarsh. "Person Re-Identification by Siamese Network." Inteligencia Artificial 26, no. 71 (2023): 25–33. http://dx.doi.org/10.4114/intartif.vol26iss71pp25-33.

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Re-Identification of person aims at retrieval of person across multiple non overlapping camera. There was a huge gain in the computer vision community with the advancement of deep learning features and also the number of surveillance in videos increased. The challenges faced by person re-identification is low resolution images, pose variation etc., and convolutional neural networks are supported by a number of state-of-the-art algorithms for person re-identification. In this paper, Siamese network is used to predict the similarity or dissimilarity of a person across two cameras. It's a neural

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Yang, Wenxi. "A Joint Network Based CNN for Yoga Pose Classification and Scoring." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 161–67. http://dx.doi.org/10.54097/hset.v23i.3218.

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Comparing to traditional rehabilitation, rehabilitation at home becomes a need during pandemic. The technique brought up in this paper allows patients and yoga fans exercise at home with low cost and comfort while can also evaluate their postures. Previous works focus either on classifying poses or scoring on the sameness between the two input branches of patients’ poses and normative poses, but they ignore the combination of them in one single network. In this study, a residual block based Siamese CNN network with classification and scoring modules is proposed, aiming at providing accurate po

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Wahyuning Wilujeng, Catherine, Erwin Hendra Putra, Wawan Hendrawan, et al. "The Role of Women to Program Agribusiness Driving in Air Talas Tourism Village (ANGGREK DEWATA)." Asian Journal of Social and Humanities 2, no. 10 (2024): 2103–10. http://dx.doi.org/10.59888/ajosh.v2i10.349.

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This research method takes the form of a qualitative field study or field research. The data collection techniques were carried out using observation with active participation, in-depth interviews, documentation and focus group discussions. The research results show: In the Agribusiness Program Driving Talas Air Tourism Villages (ANGGREK DEWATA) there are 3 (three) programs carried out by PT Pertamina EP Limau Field, namely: BU JUSI (Organic Cultivation of Siamese Oranges), BUDE ARTA MAJU (Women of Air Talas Village Managing Oranges) and PUTERI JELITA (Liquid Fertilizer from Air Talas Orange W

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Setia, Vaibhav, and Shreya Kumar. "Image Deblurring using Wiener Filtering and Siamese Neural Network." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 9, no. 3 (2021): 96. http://dx.doi.org/10.3991/ijes.v9i3.23961.

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Blurred images are difficult to avoid in situations when minor Atmospheric turbulence or camera movement results in low-quality images. We propose a system that takes a blurred image as input and produces a deblurred image by utilizing various filtering techniques. Additionally, we utilize the Siamese Network to match local image segments. A Siamese Neural Network model is used that is trained to account for image matching in the spatial domain. The best-matched image returned by the model is then further used for Signal-to-Noise ratio and Point Spread Function estimation. The Wiener filter is

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Alrashidi, Adhwa, Ashwaq Alotaibi, Muhammad Hussain, Helala AlShehri, Hatim A. AboAlSamh, and George Bebis. "Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning." Sensors 21, no. 11 (2021): 3657. http://dx.doi.org/10.3390/s21113657.

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The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper p

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Gao, Peng, Xiao Liu, Hong-Chuan Sang, Yu Wang, and Fei Wang. "Efficient and Lightweight Visual Tracking with Differentiable Neural Architecture Search." Electronics 12, no. 17 (2023): 3623. http://dx.doi.org/10.3390/electronics12173623.

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Over the last decade, Siamese network architectures have emerged as dominating tracking paradigms, which have led to significant progress. These architectures are made up of a backbone network and a head network. The backbone network comprises two identical feature extraction sub-branches, one for the target template and one for the search candidate. The head network takes both the template and candidate features as inputs and produces a local similarity score for the target object in each location of the search candidate. Despite promising results that have been attained in visual tracking, c

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Cai, Yongping, Yuefeng Cen, Gang Cen, Xiaomin Yao, Cheng Zhao, and Yulai Zhang. "Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM." World Electric Vehicle Journal 12, no. 2 (2021): 57. http://dx.doi.org/10.3390/wevj12020057.

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Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicles due to their simple structure, small size, and high power-density. The research on the temperature monitoring of the PMSMs, which is one of the critical technologies to ensure the operation of PMSMs, has been the focus. A Pseudo-Siamese Nested LSTM (PSNLSTM) model is proposed to predict the temperature of the PMSMs. It takes the features closely related to the temperature of PMSMs as input and realizes the temperature prediction of stator yoke, stator tooth, and stator winding. An optimization algorithm of learnin

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Zhang, Bo, Zhixue Liang, and Wenyong Dong. "Siamese Attention Networks with Adaptive Templates for Visual Tracking." Mobile Information Systems 2022 (September 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/7056149.

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Visual object tracking takes an important role in realistic applications, such as video understanding, unmanned auto vehicles, and autonomous robots. Although the Siamese-based tracker has achieved good performance in tracking tasks, the existing methods using initial template or updating template with simple strategy result in the performance degradation of the model when the target varies in realistic scenarios such as target occlusion, scale variation, and deformation. In this paper, we propose a visual tracking framework with adaptive template update and spatiotemporal attention, named Sia

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Lan, Meng, Jing Zhang, Fengxiang He, and Lefei Zhang. "Siamese Network with Interactive Transformer for Video Object Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1228–36. http://dx.doi.org/10.1609/aaai.v36i2.20009.

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Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years. The key challenge lies in finding effective ways to exploit the spatio-temporal context of past frames to help learn discriminative target representation of current frame. In this paper, we propose a novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames. Technically, we use the transformer e

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Duma, Vony Christin Pato, I. Dewa Gde Mayun Permana, and Luh Putu Trisna Darmayanti. "Pola Pertumbuhan Mikroba Dominan pada Fermentasi Alami Buah Jeruk Siam (Citrus nobilis) untuk Produksi Arak." Jurnal Ilmu dan Teknologi Pangan (ITEPA) 14, no. 1 (2025): 98. https://doi.org/10.24843/itepa.2025.v14.i01.p08.

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Awan Village is one of the abundant siamese orange (Citrus nobilis) producing villages in Kintamani District, Bangli Regency. The abundance of siamese orange is utilized by Awan Village by processing siam oranges into orange arak as an attempt to diversify citrus fruit products. Orange arak is one of the processed products of Kintamani siam oranges processed through a fermentation and distillation process. UMKM Tirta Kahuripan as an orange arak producer in Awan Village uses natural fermentation process for 21 days. Natural fermentation generally involves many types of microbes, but in natural

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15

Rani, Seema, Avadhesh Kumar, and Naresh Kumar. "Eliminating Data Duplication in CQA Platforms Using Deep Neural Model." Computational Intelligence and Neuroscience 2022 (August 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/2067449.

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Primary research to detect duplicate question pairs within community-based question answering systems is based on datasets made of English questions only. This research put forward a solution to the problem of duplicate question detection by matching semantically identical questions in transliterated bilingual data. Deep learning has been implemented to analyze informal languages like Hinglish which is a bilingual mix of Hindi and English on Community Question Answering (CQA) platforms to identify duplicacy in questions. The proposed model works in two sequential modules. First module is a lan

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Nanni, Loris, Giovanni Minchio, Sheryl Brahnam, Davide Sarraggiotto, and Alessandra Lumini. "Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks." Sensors 21, no. 17 (2021): 5809. http://dx.doi.org/10.3390/s21175809.

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In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering

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K., B. Low, and U. Sheikh U. "Human Re-identification with Global and Local Siamese Convolution Neural Network." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 2 (2017): 726–32. https://doi.org/10.12928/TELKOMNIKA.v15i2.6121.

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Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply handcrafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convoluti

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Li, Ji, Chunxiang Gu, Fushan Wei, et al. "LightSEEN: Real-Time Unknown Traffic Discovery via Lightweight Siamese Networks." Security and Communication Networks 2021 (October 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/8267298.

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With the increase in the proportion of encrypted network traffic, encrypted traffic identification (ETI) is becoming a critical research topic for network management and security. At present, ETI under closed world assumption has been adequately studied. However, when the models are applied to the realistic environment, they will face unknown traffic identification challenges and model efficiency requirements. Considering these problems, in this paper, we propose a lightweight unknown traffic discovery model LightSEEN for open-world traffic classification and model update under practical condi

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19

Liu, Xinpu, Yanxin Ma, Ke Xu, Ling Wang, and Jianwei Wan. "KASiam: Keypoints-Aligned Siamese Network for the Completion of Partial TLS Point Clouds." Remote Sensing 14, no. 15 (2022): 3617. http://dx.doi.org/10.3390/rs14153617.

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Completing point clouds from partial terrestrial laser scannings (TLS) is a fundamental step for many 3D visual applications, such as remote sensing, digital city and autonomous driving. However, existing methods mainly followed an ordinary auto-encoder architecture with only partial point clouds as inputs, and adopted K-Nearest Neighbors (KNN) operations to extract local geometric features, which takes insufficient advantage of input point clouds and has limited ability to extract features from long-range geometric relationships, respectively. In this paper, we propose a keypoints-aligned sia

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20

Lee, Jimi, Jaeyeal Nam, and ByoungChul Ko. "Multiple pedestrian tracking using Siamese random forests and shallow Convolutional Neural Networks." Electronic Imaging 2020, no. 16 (2020): 108–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-088.

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In this study, we propose a new multi-pedestrian tracking (MPT) method that performs quickly and efficiently track pedestrians in real-time system. The proposed method considers combining shallow convolutional neural networks (CNN) with ensemble learning method, Siamese random forests (SRF). Unlike conventional methods, to promote robustness of ensemble method, feature transformation is applied which exploit shallow networks in appearances of still images to extract enrich features. We formulate the problem of MOT in a structured learning framework based on SRF. Each forest learns differences

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Kurek, Jarosław, Izabella Antoniuk, Bartosz Świderski, Albina Jegorowa, and Michał Bukowski. "Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes." Sensors 20, no. 23 (2020): 6978. http://dx.doi.org/10.3390/s20236978.

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In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a poor quality product and therefore generate some financial loss for the company. In various approaches to this problem, usually, three classes are considered: green for a drill that is sharp, red for the opposite, and yellow for a tool that is suspected of being worn out, requiring additional evaluati

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Ferrara, Federico. "The legend of King Prajadhipok: Tall tales and stubborn facts on the seventh reign in Siam." Journal of Southeast Asian Studies 43, no. 1 (2012): 4–31. http://dx.doi.org/10.1017/s0022463411000646.

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The figure of King Prajadhipok (r. 1925–35), Siam's last absolute monarch, remains of great significance to Thailand's contemporary political discourse. King Prajadhipok's historical role as the ‘founding father’ of Thai democracy, in particular, lies at the heart of the Chakri dynasty's claim to democratic legitimacy — a claim that is now widely questioned, both at home and abroad. This article re-examines King Prajadhipok's conduct in the early days of constitutional government in Siam. While the King's status as the father of Thai democracy is exposed as a myth, his actual historical legacy

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Ahmed, Nehal K., Elsayed E. Hemayed, and Magda B. Fayek. "Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources." Big Data and Cognitive Computing 4, no. 3 (2020): 19. http://dx.doi.org/10.3390/bdcc4030019.

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In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, w

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Jiang, Lingyun, Kai Qiao, Linyuan Wang, et al. "Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare." Applied Sciences 9, no. 22 (2019): 4749. http://dx.doi.org/10.3390/app9224749.

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Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired b

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Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, et al. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.

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Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improv

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Perera, Chamika Janith, Chinthaka Premachandra, and Hiroharu Kawanaka. "Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks." Sensors 23, no. 18 (2023): 8004. http://dx.doi.org/10.3390/s23188004.

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Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in ge

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Nassar, Ahmed Samy, Sébastien Lefèvre, and Jan Dirk Wegner. "Multi-View Instance Matching with Learned Geometric Soft-Constraints." ISPRS International Journal of Geo-Information 9, no. 11 (2020): 687. http://dx.doi.org/10.3390/ijgi9110687.

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We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of

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Rao, Mengbin, Ping Tang, and Zheng Zhang. "A Developed Siamese CNN with 3D Adaptive Spatial-Spectral Pyramid Pooling for Hyperspectral Image Classification." Remote Sensing 12, no. 12 (2020): 1964. http://dx.doi.org/10.3390/rs12121964.

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Since hyperspectral images (HSI) captured by different sensors often contain different number of bands, but most of the convolutional neural networks (CNN) require a fixed-size input, the generalization capability of deep CNNs to use heterogeneous input to achieve better classification performance has become a research focus. For classification tasks with limited labeled samples, the training strategy of feeding CNNs with sample-pairs instead of single sample has proven to be an efficient approach. Following this strategy, we propose a Siamese CNN with three-dimensional (3D) adaptive spatial-s

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Wang, Zhifan, Tong Xin, Shidong Wang, and Haofeng Zhang. "Depth-embedded instance segmentation network for urban scene parsing." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1269–79. http://dx.doi.org/10.3233/jifs-202230.

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The ubiquitous availability of cost-effective cameras has rendered large scale collection of street view data a straightforward endeavour. Yet, the effective use of these data to assist autonomous driving remains a challenge, especially lack of exploration and exploitation of stereo images with abundant perceptible depth. In this paper, we propose a novel Depth-embedded Instance Segmentation Network (DISNet) which can effectively improve the performance of instance segmentation by incorporating the depth information of stereo images. The proposed network takes binocular images as input to obse

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Gema, Aryo Pradipta, Suhendro Winton, Theodorus David, Derwin Suhartono, Muhsin Shodiq, and Wikaria Gazali. "It Takes Two To Tango: Modification of Siamese Long Short Term Memory Network with Attention Mechanism in Recognizing Argumentative Relations in Persuasive Essay." Procedia Computer Science 116 (2017): 449–59. http://dx.doi.org/10.1016/j.procs.2017.10.036.

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Sun, Chengzhe, Jiangjiang Wu, Hao Chen, and Chun Du. "SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention." Remote Sensing 14, no. 12 (2022): 2801. http://dx.doi.org/10.3390/rs14122801.

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Change detection (CD) is one of the important applications of remote sensing and plays an important role in disaster assessment, land use detection, and urban sprawl tracking. High-accuracy fully supervised methods are the main methods for CD tasks at present. However, these methods require a large amount of labeled data consisting of bi-temporal images and their change maps. Moreover, creating change maps takes a lot of labor and time. To address this limitation, a simple semi-supervised change detection method based on consistency regularization and strong augmentation is proposed in this pa

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Dong, Yihan, Hanming Quan, Chenxi Ma, Linchao Shan, and Lei Deng. "TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning." International Journal of Molecular Sciences 25, no. 13 (2024): 7228. http://dx.doi.org/10.3390/ijms25137228.

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In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein s

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Islam, Towhidul, Md Mahedi Hasan Rigan, Md Olid Hasan Bhuiyan, Tanzima Hashem, and Md Mahbubur Rahman. "H2OPulse: Smartphone-assisted Vein Evaluation for Early Recognition of Dehydration." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 9, no. 2 (2025): 1–24. https://doi.org/10.1145/3729490.

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Maintaining human health relies on adequate water intake for digestion, waste elimination, and temperature regulation. Dehydration occurs when the body loses more fluids than it takes in, leading to electrolyte imbalances and serious complications, especially in vulnerable populations. Traditional dehydration detection methods rely on laboratory tests and physical exams, which can be invasive, expensive, time-consuming, and often inaccessible in rural areas. Recent research has proposed several solutions, but these are often either unreliable or not widely accessible. We introduce H2OPulse, th

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Shuai, Wenjing, Fenlong Jiang, Hanhong Zheng, and Jianzhao Li. "MSGATN: A Superpixel-Based Multi-Scale Siamese Graph Attention Network for Change Detection in Remote Sensing Images." Applied Sciences 12, no. 10 (2022): 5158. http://dx.doi.org/10.3390/app12105158.

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With the rapid development of Earth observation technology, how to effectively and efficiently detect changes in multi-temporal images has become an important but challenging problem. Relying on the advantages of high performance and robustness, object-based change detection (CD) has become increasingly popular. By analyzing the similarity of local pixels, object-based CD aggregates similar pixels into one object and takes it as the basic processing unit. However, object-based approaches often have difficulty capturing discriminative features, as irregular objects make processing difficult. To

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Zhao, Enmin, Renye Yan, Jinqiu Li, Kai Li, and Junliang Xing. "AlphaHoldem: High-Performance Artificial Intelligence for Heads-Up No-Limit Poker via End-to-End Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4689–97. http://dx.doi.org/10.1609/aaai.v36i4.20394.

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Heads-up no-limit Texas hold’em (HUNL) is the quintessential game with imperfect information. Representative priorworks like DeepStack and Libratus heavily rely on counter-factual regret minimization (CFR) and its variants to tackleHUNL. However, the prohibitive computation cost of CFRiteration makes it difficult for subsequent researchers to learnthe CFR model in HUNL and apply it in other practical applications. In this work, we present AlphaHoldem, a high-performance and lightweight HUNL AI obtained with an end-to-end self-play reinforcement learning framework. The proposed framework adopts

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Gao, Shouwan, Jianan He, Honghua Pan, and Tao Gong. "A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples." Symmetry 14, no. 5 (2022): 909. http://dx.doi.org/10.3390/sym14050909.

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Currently, deep-learning-based methods have been widely used in fault diagnosis to improve the diagnosis efficiency and intelligence. However, most schemes require a great deal of labeled data and many iterations for training parameters. They suffer from low accuracy and over fitting under the few-shot scenario. In addition, a large number of parameters in the model consumes high computing resources, which is far from practical. In this paper, a multi-scale and lightweight Siamese network architecture is proposed for the fault diagnosis with few samples. The architecture proposed contains two

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Liu, Xia, Minghui Wang, and Ao Li. "PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information." PeerJ 10 (March 14, 2022): e12847. http://dx.doi.org/10.7717/peerj.12847.

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Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which

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Borghi, Guido, Stefano Pini, Roberto Vezzani, and Rita Cucchiara. "Driver Face Verification with Depth Maps." Sensors 19, no. 15 (2019): 3361. http://dx.doi.org/10.3390/s19153361.

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Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external lig

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SEDRINE, Mohamed Ali, Wided SOUIDENE MSEDDI, and Rabah ATTIA. "NEURAL NETWORK VISUAL ODOMETRY BASED FRAMEWORK FOR UAV LOCALIZATION IN GPS DENIED ENVIRONMENT." COMPUSOFT: An International Journal of Advanced Computer Technology 09, no. 08 (2020): 3798–809. https://doi.org/10.5281/zenodo.15024902.

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This paper presents a vision-based localization framework based on visual odometry.Visual odometry is a classic approach to incrementally estimate robot motion even in GPS denied environment, by tracking features in successive images. As it is subject to drift, this paper proposes to call a convolutional neural netwok and visual memory to improve process accuracy. In fact, our framework is made of two main steps. First, the robot builds its visual memory by annotating places with their ground truth positions. Dedicated data structures are made to store referenced images and their positions. Th

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Burawoy, Michael. "The Extended Case Method." Sociological Theory 16, no. 1 (1998): 4–33. http://dx.doi.org/10.1111/0735-2751.00040.

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In this article I elaborate and codify the extended case method, which deploys participant observation to locate everyday life in its extralocal and historical context. The extended case method emulates a reflexive model of science that takes as its premise the intersubjectivity of scientist and subject of study. Reflexive science valorizes intervention, process, structuration, and theory reconstruction. It is the Siamese twin of positive science that proscribes reactivity, but upholds reliability, replicability, and representativeness. Positive science, exemplified by survey research, works o

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Albasu, F. B., and M. A. Al Akkad. "Exploiting Deep Learning Techniques for the Verification of Handwritten Signatures." Intellekt. Sist. Proizv. 21, no. 3 (2023): 27–39. http://dx.doi.org/10.22213/2410-9304-2023-3-27-39.

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Biometric featuresare common measures of identity verification where signaturesarethe most used type. The digital technology has given birth to new ways of biometric identification, such as fingerprints, iris and face recognition,while dealing with handwritten signatures is still a challenging task, because handwritten signatures are more prone to forgery than other means of verification due to issues like computer error, insufficient datasets, and loss of information. This work aims to develop a system that takes a signature image as its input and determines whether the signature is genuine w

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Li, Yi, Zhe Wu, Shuang Zhao, et al. "PSENet: Psoriasis Severity Evaluation Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 800–807. http://dx.doi.org/10.1609/aaai.v34i01.5424.

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Psoriasis is a chronic skin disease which affects hundreds of millions of people around the world. This disease cannot be fully cured and requires lifelong caring. If the deterioration of Psoriasis is not detected and properly treated in time, it could cause serious complications or even lead to a life threat. Therefore, a quantitative measurement that can track the Psoriasis severity is necessary. Currently, PASI (Psoriasis Area and Severity Index) is the most frequently used measurement in clinical practices. However, PASI has the following disadvantages: (1) Time consuming: calculating PASI

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Wang, Congcong, Wenbin Sun, Deqin Fan, Xiaoding Liu, and Zhi Zhang. "Adaptive Feature Weighted Fusion Nested U-Net with Discrete Wavelet Transform for Change Detection of High-Resolution Remote Sensing Images." Remote Sensing 13, no. 24 (2021): 4971. http://dx.doi.org/10.3390/rs13244971.

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The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weig

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Kim, Jiyeon, and Youngok Kang. "Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering." ISPRS International Journal of Geo-Information 11, no. 4 (2022): 245. http://dx.doi.org/10.3390/ijgi11040245.

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With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology h

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Anggaini, Niken, Anita Tri Widiyawati, Faizatul Amalia, Romula Adiono, and Novita Islami. "Pendampingan Emergency Management Arsip dalam Mendukung Terwujudnya Desa Peduli Arsip." Bubungan Tinggi: Jurnal Pengabdian Masyarakat 5, no. 1 (2023): 151. http://dx.doi.org/10.20527/btjpm.v5i1.6794.

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Desa Paseban yang berada di Kabupaten Jember merupakan daerah rawan bencana tsunami, banjir genangan, puting beliung, gempa bumi, kebakaran, abrasi pantai, kelalaian manusia, dan juga bencana sosial. Potensi bencana di Desa Paseban juga dapat berdampak pada arsip Pemerintah Desa Paseban. Tujuan dari kegiatan ini adalah untuk membuat perangkat desa memiliki kemampuan dalam mengelola arsip melalui emergency management arsip dengan bantuan aplikasi. Metode pelaksanaan kegiatan ini melalui teori yang diberikan dilanjutkan dengan praktik. Pelaksanaan pengabdian kepada masyarakat ini membutuhkan wak

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Xiang, Deliang, Yihao Xu, Jianda Cheng, Yuzhen Xie, and Dongdong Guan. "Progressive Keypoint Detection with Dense Siamese Network for SAR Image Registration." IEEE Transactions on Aerospace and Electronic Systems, 2023, 1–12. http://dx.doi.org/10.1109/taes.2023.3266415.

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Jia, Zhen, Zhenbao Liu, Zhifei Li, Kai Wang, and Chi-Man Vong. "Lightweight Fault Diagnosis via Siamese Network for Few-Shot EHA Circuit Analysis." IEEE Transactions on Aerospace and Electronic Systems, 2025, 1–14. https://doi.org/10.1109/taes.2025.3589534.

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Jia, Yizhen, Mengmeng He, Hui Chen, and Wen-Qin Wang. "Joint Design of Transmit Waveform and Receive Filter for Suppression of Mainlobe Deceptive Jammer via Deep Transfer Siamese Network." IEEE Transactions on Aerospace and Electronic Systems, 2025, 1–17. https://doi.org/10.1109/taes.2025.3572062.

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Rajkumar, K., and B. Paramasivan. "Redundant node identification using Siamese neural network to enhance the availability in visual sensor network." International Journal of Communication Systems, November 5, 2023. http://dx.doi.org/10.1002/dac.5655.

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SummaryMany monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gatew

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Li, Qing, and Song He. "Similarity matching of medical question based on Siamese network." BMC Medical Informatics and Decision Making 23, no. 1 (2023). http://dx.doi.org/10.1186/s12911-023-02161-z.

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Abstract Background With the rapid development of the medical industry and the gradual increase in people’s awareness of their health, the use of the Internet for medical question and answer, to obtain more accurate medical answers. It is necessary to first calculate the similarity of the questions asked by users, which further matches professional medical answers. Improving the efficiency of online medical question and answer sessions will not only reduce the burden on doctors, but also enhance the patient’s experience of online medical diagnosis. Method This paper focuses on building a bidir

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