Relevant bibliographies by topics / K-shot classification

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Academic literature on the topic 'K-shot classification'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "K-shot classification"

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Khaled, Alzamel, and Alajmi Manayer. "Few-shot Learning Approach for Arabic Scholarly Paper Classification using SetFit Framework." WSEAS TRANSACTIONS ON COMMUNICATIONS 23 (December 27, 2024): 89–95. https://doi.org/10.37394/23204.2024.23.12.

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Focus on the few-shot approach has increased recently for TC as it is competitive with fine-tuning models that need a large dataset [14]. In NLP, the process of using PTMs to classify new data is preferable to the expensive process of training a model from scratch. This can be considered a kind of TL, i.e., it focuses on reusing knowledge of PTMs to solve different problems, as long as the pre-training data is appropriately comparable. Transferring knowledge allows the model to circumvent the lack of data and enable FSL as a low-cost solution. To clarify, the term shot refers to a single examp
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Minhas, Rabia A., Ali Javed, Aun Irtaza, Muhammad Tariq Mahmood, and Young Bok Joo. "Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network." Applied Sciences 9, no. 3 (2019): 483. http://dx.doi.org/10.3390/app9030483.

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Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the
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Thomas, Hugo, Guillaume Gravier, and Pascale Sébillot. "One-shot relation retrieval in news archives: adapting N-way K-shot relation Classification for efficient knowledge extraction." Procedia Computer Science 246 (2024): 1060–69. http://dx.doi.org/10.1016/j.procs.2024.09.525.

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Rizinski, Maryan, Andrej Jankov, Vignesh Sankaradas, Eugene Pinsky, Igor Mishkovski, and Dimitar Trajanov. "Comparative Analysis of NLP-Based Models for Company Classification." Information 15, no. 2 (2024): 77. http://dx.doi.org/10.3390/info15020077.

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The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classifica
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Tian, Pinzhuo, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, and Yang Gao. "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12087–94. http://dx.doi.org/10.1609/aaai.v34i07.6887.

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To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i.e., one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K > 1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classific
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Yanas Rajindran and Hanza Parayil Salim. "A Comparative Analysis of Clustering Methods on the 20 Newsgroups Dataset for Analytics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3075–78. https://doi.org/10.32628/cseit25112788.

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This paper presents a comparative analysis of two different approaches for clustering textual data from the 20 Newsgroups dataset. The first approach leverages a Large Language Model (LLM) to classify each text into predefined categories using zero-shot classification. The second approach applies to the traditional K-Means clustering algorithm on text embeddings. We evaluate both methods by comparing their predicted clusters against true labels for assessment. For K-Means, we also explore a semi-supervised variant with centroid initialization based on true labels.
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Wang, Miaorui. "Few-shot Image Information Mining and Its Application in UAV Early Warning." Applied and Computational Engineering 150, no. 1 (2025): 155–65. https://doi.org/10.54254/2755-2721/2025.22704.

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Artificial intelligence applications in vertical fields face the problem of a small and unbalanced number of annotated samples. This article attempts to solve the small sample problem from three perspectives: data, model, and algorithm, by selecting UAV early warning. For data, conventional image enhancement, Mixup enhancement, random masking, and Partial convolution techniques are used to enhance the paradigm of Few-shot samples. Transfer learning techniques are applied to algorithms. Firstly, an autoencoder is trained using many unlabeled images combined with a masking strategy. Secondly, a
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Seo, Minjo, and Hyunsoo Kim. "Irregular Openings Identification at Construction Sites Based on Few-Shot Learning." Buildings 15, no. 11 (2025): 1834. https://doi.org/10.3390/buildings15111834.

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The construction industry frequently encounters safety hazards, with falls related to undetected openings being a major cause of fatalities. Identifying unstructured openings using computer vision is challenging due to their unpredictable nature and the difficulty of acquiring large labeled datasets in dynamic construction environments. Conventional deep learning methods require substantial data, limiting their applicability. Few-shot learning (FSL) offers a promising alternative by enabling models to learn from limited examples. This study investigates the effectiveness of an FSL approach, sp
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Wang, Aili, Chengyang Liu, Dong Xue, Haibin Wu, Yuxiao Zhang, and Meihong Liu. "Hyperspectral Image Classification Based on Cross-Scene Adaptive Learning." Symmetry 13, no. 10 (2021): 1878. http://dx.doi.org/10.3390/sym13101878.

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Aiming at few-shot classification in the field of hyperspectral remote sensing images, this paper proposes a classification method based on cross-scene adaptive learning. First, based on the unsupervised domain adaptive technology, cross-scene knowledge transfer learning is carried out to reduce the differences between source scene and target scene. At the same time, depthwise over-parameterized convolution is used in the deep embedding model to improve the convergence speed and feature extraction ability. Second, two symmetrical subnetworks are designed in the model to further reduce the diff
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Li, Liangwei, Lin Liu, Xiaohui Du, et al. "CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification." Sensors 22, no. 24 (2022): 9980. http://dx.doi.org/10.3390/s22249980.

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Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. H
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Book chapters on the topic "K-shot classification"

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Labiod, Lazhar, and Mohamed Nadif. "Towards a Bi-Stochastic Matrix Approximation of k-means and Some Variants." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_24.

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AbstractThe k-means algorithm and some k-means variants have been shown to be useful and effective to tackle the clustering problem. In this paper we embed k-means variants in a bi-stochastic matrix approximation (BMA) framework. Then we derive from the k-means objective function a new formulation of the criterion. In particular, we show that some k-means variants are equivalent to algebraic problem of bi-stochastic matrix approximation under some suitable constraints. For optimizing the derived objective function, we develop two algorithms; the first one consists in learning a bi-stochastic s
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Tahiri, Nadia, and Aleksandr Koshkarov. "New Metrics for Classifying Phylogenetic Trees Using K-means and the Symmetric Difference Metric." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_41.

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AbstractThe k-means method can be adapted to any type of metric space and is sometimes linked to the median procedures. This is the case for symmetric difference metric (or Robinson and Foulds) distance in phylogeny, where it can lead to median trees as well as to Euclidean Embedding. We show how a specific version of the popular k-means clustering algorithm, based on interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data is homogeneous) or several (when the data is heterogeneous) cluster(s) of trees. We hav
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Ounit, Rachid, and Stefano Lonardi. "Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48221-6_21.

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Li, Yannan, Jingbo Wang, and Chao Wang. "Certifying the Fairness of KNN in the Presence of Dataset Bias." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37703-7_16.

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AbstractWe propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group. To the best of our knowledge, this is the first certification method for KNN based on three variants of the fairness definition: individual fairness, $$\epsilon $$ ϵ -fairness, and label-flipping fairness. We first define the fairness certification problem for KNN and then propose sound ap
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Do, Dai, Quan Tran, Svetha Venkatesh, and Hung Le. "Large Language Model Prompting with Episodic Memory." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240953.

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Prompt optimization is essential for enhancing the performance of Large Language Models (LLMs) in a range of Natural Language Processing (NLP) tasks, particularly in scenarios of few-shot learning where training examples are incorporated directly into the prompt. Despite the growing interest in optimizing prompts with few-shot examples, existing methods for prompt optimization are often resource-intensive or perform inadequately. In this work, we propose PrOmpting with Episodic Memory (POEM), a novel prompt optimization technique that is simple, efficient, and demonstrates strong generalizatio
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Roy, Soumyadeep, Jonas Wallat, Sowmya S. Sundaram, Wolfgang Nejdl, and Niloy Ganguly. "GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230492.

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Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and
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Roy, Soumyadeep, Shamik Sural, and Niloy Ganguly. "Unlocking Efficiency: Adaptive Masking for Gene Transformer Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240864.

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Gene transformer models such as Nucleotide Transformer, DNABert, and LOGO are trained to learn optimal gene sequence representations by using the Masked Language Modeling (MLM) training objective over the complete Human Reference Genome. However, the typical tokenization methods employ a basic sliding window of tokens, such as k-mers, that fail to utilize gene-centric semantics. This could result in the (trivial) masking of easily predictable sequences, leading to inefficient MLM training. Time-variant training strategies are known to improve pretraining efficiency in both language and vision
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Bai, Junjie, Kan Luo, Jun Peng, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch052.

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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nea
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Connolly, Andrew J., Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray. "Classification." In Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691151687.003.0009.

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Chapter 6 described techniques for estimating joint probability distributions from multivariate data sets and for identifying the inherent clustering within the properties of sources. This approach can be viewed as the unsupervised classification of data. If, however, we have labels for some of these data points (e.g., an object is tall, short, red, or blue) we can utilize this information to develop a relationship between the label and the properties of a source. We refer to this as supervised classification, which is the focus of this chapter. The motivation for supervised classification com
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Swetha Y and Kalaiarasi S. "Damaged Licensed Number Plate Detection Based on Novel Object Oriented Classification with K-Means Algorithm." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220077.

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The aim is to detect the quality of number plates using object oriented classification in comparison with K-Means clustering. Two groups such as novel Object Oriented Classification and K-Means algorithm are applied. Total number of samples that are evaluated on this proposed methodology are 265 images. Among this sample dataset, 185 images [70%] of the dataset was taken as a training dataset and 80 [30%] was taken as a testing dataset. Programming experiment was carried out for N=7 and N=9 iterations for novel Object Oriented Classification and K-Means algorithm respectively. Computation proc
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Conference papers on the topic "K-shot classification"

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Han, Ke, and Adrian Barbu. "Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649996.

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S, Tilak Bala, and Rajeswari M. "Innovative Financial Fraud Detection: Combining GCRNN and DiffPool with N-Way K-Shot Classification Techniques." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841755.

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Helsel, Jayson L., Michael F. Melampy, and Kirk Wissmar. "Expected Service Life and Cost Considerations for Maintenance and New Construction Protective Coating Work." In CORROSION 2006. NACE International, 2006. https://doi.org/10.5006/c2006-06318.

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Abstract This paper is a significant update to “Costing Considerations for Maintenance and New Construction Coating Work”1 on protective coating costing and selection co-authored by M. F. Melampy, M. P. Reina and K. R. Shields in 1998. Designed to assist the coatings engineer or specifier in identifying suitable protective coating systems for specific industrial environments, this paper provides guidelines for calculating approximate installed costs of coating systems, expected coating service lives for each system identified, and methods for determining the most cost-effective systems to use.
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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application o
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van der Heijden, Niels, Ekaterina Shutova, and Helen Yannakoudakis. "K-hop neighbourhood regularization for few-shot learning on graphs: A case study of text classification." In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.eacl-main.85.

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Liu, Lu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/418.

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A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototyp
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Xie, Tingli, Xufeng Huang, and Seung-Kyum Choi. "Information Fusion-Based Meta-Learning for Few-Shot Fault Diagnosis Under Different Working Conditions." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90934.

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Abstract With the development of deep learning and information technologies, intelligent fault diagnosis has been further developed, which achieves satisfactory identification of mechanical faults. However, the lack of labeled samples and complex working conditions can hinder the improvement of diagnostics models. In this article, a novel method called Information Fusion-based Meta-Learning (IFML) is explored for fault diagnosis with few-shot problems under different working conditions. Firstly, an information fusion and embedding module is applied to perform both data- and feature-level fusio
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Wang, Junyang, Ming Yan, Yi Zhang, and Jitao Sang. "From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/481.

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With the development of Vision-Language Pre-training Models (VLPMs) represented by CLIP and ALIGN, significant breakthroughs have been achieved for association-based visual tasks such as image classification and image-text retrieval by the zero-shot capability of CLIP without fine-tuning. However, CLIP is hard to apply to generation-based tasks. This is due to the lack of decoder architecture and pre-training tasks for generation. Although previous works have created generation capacity for CLIP through additional language models, a modality gap between the CLIP representations of different mo
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Bäcker, Paul, Georg Maier, Robin Gruna, Thomas Längle, and Jürgen Beyerer. "Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis." In OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings. KIT Scientific Publishing, 2023. http://dx.doi.org/10.58895/ksp/1000155014-2.

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Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant
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ALTABEY, WAEL A., MOHAMMAD NOORI, ZHISHEN WU, AHMED SILIK, and VASILIS SARHOSIS. "ENHANCEMENT OF STRUCTURAL HEALTH MONITORING FRAMEWORK ON BEAMS BASED ON K-NEAREST NEIGHBOR ALGORITHM." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37068.

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The aiming of this work is to enhancement the structural health monitoring (SHM) framework of beams structure for damage detection to treatment the drawbacks of poor detection efficiency in traditional of beams monitoring algorithms, the improvement framework on beams SHM is based on novel data classification technique through designing the k-Nearest Neighbor (k-NN) algorithm. First, the beam finite element model under impact load is analysis, and the cumulative damages are considered and introduced to beam model. The datasets of beam SHM are compiled from the sensors installed in beam structu
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Reports on the topic "K-shot classification"

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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