Log in

Relevant bibliographies by topics / Cross-Domain Few-Shot Learning / Journal articles

To see the other types of publications on this topic, follow the link: Cross-Domain Few-Shot Learning.

Journal articles on the topic 'Cross-Domain Few-Shot Learning'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Cross-Domain Few-Shot Learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Hassani, Kaveh. "Cross-Domain Few-Shot Graph Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6856–64. http://dx.doi.org/10.1609/aaai.v36i6.20642.

Full text

Abstract:

We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views, to learn representations of task-specific information for fast adaptation, and task-agnostic information for knowledge transfer. We run exhaustive experiments to evaluate the performance of contrastive and meta-learning strategies. We show that when coupled with metri

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, Qi, Yingluo Jiang, and Zhijie Wen. "TACDFSL: Task Adaptive Cross Domain Few-Shot Learning." Symmetry 14, no. 6 (2022): 1097. http://dx.doi.org/10.3390/sym14061097.

Full text

Abstract:

Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal distribution difference between two domains which is implicit and unknown. So the empirical marginal distribution measurement is proposed, that is, WDMDS (Wasserstein Distance for Measuring Domain Shift) and MMDMDS (Maximum Mean Discrepancy for Measuring Domain Shift). Besides this, pre-training a feature extractor and fine-tuning a classifie

APA, Harvard, Vancouver, ISO, and other styles

3

Paeedeh, Naeem, Mahardhika Pratama, Muhammad Anwar Ma’sum, Wolfgang Mayer, Zehong Cao, and Ryszard Kowlczyk. "Cross-domain few-shot learning via adaptive transformer networks." Knowledge-Based Systems 288 (March 2024): 111458. http://dx.doi.org/10.1016/j.knosys.2024.111458.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Kang, Suhyun, Jungwon Park, Wonseok Lee, and Wonjong Rhee. "Task-Specific Preconditioner for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17760–69. https://doi.org/10.1609/aaai.v39i17.33953.

Full text

Abstract:

Cross-Domain Few-Shot Learning (CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches have utilized fixed optimization strategies, despite their potential sub-optimality across varying domains or target tasks. To address this issue, we propose a novel adaptation mechanism called Task-Specific Preconditioned gradient descent (TSP). Our method first meta-learns Domain-Specific Preconditioners (DSPs) that capture the characteristics of each meta-training domain, which are then linearly combined using task

APA, Harvard, Vancouver, ISO, and other styles

5

Wawer, Aleksander. "Few-Shot Methods for Aspect-Level Sentiment Analysis." Information 15, no. 11 (2024): 664. http://dx.doi.org/10.3390/info15110664.

Full text

Abstract:

In this paper, we explore the approaches to the problem of cross-domain few-shot classification of sentiment aspects. By cross-domain few-shot, we mean a setting where the model is trained on large data in one domain (for example, hotel reviews) and is intended to perform on another (for example, restaurant reviews) with only a few labelled examples in the target domain. We start with pre-trained monolingual language models. Using the Polish language dataset AspectEmo, we compare model training using standard gradient-based learning to a zero-shot approach and two dedicated few-shot methods: P

APA, Harvard, Vancouver, ISO, and other styles

6

Yuan, Wang, Zhizhong Zhang, Cong Wang, Haichuan Song, Yuan Xie, and Lizhuang Ma. "Task-Level Self-Supervision for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3215–23. http://dx.doi.org/10.1609/aaai.v36i3.20230.

Full text

Abstract:

Learning with limited labeled data is a long-standing problem. Among various solutions, episodic training progres-sively classifies a series of few-shot tasks and thereby is as-sumed to be beneficial for improving the model’s generalization ability. However, recent studies show that it is eveninferior to the baseline model when facing domain shift between base and novel classes. To tackle this problem, we pro-pose a domain-independent task-level self-supervised (TL-SS) method for cross-domain few-shot learning.TL-SS strategy promotes the general idea of label-based instance-levelsupervision to

APA, Harvard, Vancouver, ISO, and other styles

7

Wu, Jiamin, Xin Liu, Xiaotian Yin, Tianzhu Zhang, and Yongdong Zhang. "Task-Adaptive Prompted Transformer for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 6012–20. http://dx.doi.org/10.1609/aaai.v38i6.28416.

Full text

Abstract:

Cross-Domain Few-Shot Learning (CD-FSL) aims at recognizing samples in novel classes from unseen domains that are vastly different from training classes, with few labeled samples. However, the large domain gap between training and novel classes makes previous FSL methods perform poorly. To address this issue, we propose MetaPrompt, a Task-adaptive Prompted Transformer model for CD-FSL, by jointly exploiting prompt learning and the parameter generation framework. The proposed MetaPrompt enjoys several merits. First, a task-conditioned prompt generator is established upon attention mechanisms. I

APA, Harvard, Vancouver, ISO, and other styles

8

Li, Xueying, Zihang He, Lingyan Zhang, Shaojun Guo, Bin Hu, and Kehua Guo. "CDCNet: Cross-domain few-shot learning with adaptive representation enhancement." Pattern Recognition 162 (June 2025): 111382. https://doi.org/10.1016/j.patcog.2025.111382.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Cui, Xiaodong, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu, and Jing Han. "Cross-Domain Contrastive Learning-Based Few-Shot Underwater Acoustic Target Recognition." Journal of Marine Science and Engineering 12, no. 2 (2024): 264. http://dx.doi.org/10.3390/jmse12020264.

Full text

Abstract:

Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the un

APA, Harvard, Vancouver, ISO, and other styles

10

Yandong, Du, Feng Lin, Tao Peng, Gong Xun, and Wang Jun. "Meta-transfer learning in cross-domain image classification with few-shot learning." Journal of Image and Graphics 28, no. 9 (2023): 2899–912. http://dx.doi.org/10.11834/jig.220664.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Zhang, Qiannan, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V. Chawla, and Xiangliang Zhang. "Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4893–901. http://dx.doi.org/10.1609/aaai.v37i4.25615.

Full text

Abstract:

Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue in graph mining problems. However, the utilization of cross-domain data induces another intractable domain shift issue which severely degrades the generalization ability of cross-domain graph few-shot learning models. The combat with the domain shift issue is hindered due to the coarse utilization of source domains and the ignorance of accessible prompts. To address these challenges, in this paper, we design a novel Cross-domain Task Coordinator to leverage a small set of labeled target domain data as pr

APA, Harvard, Vancouver, ISO, and other styles

12

Tang, Haojin, Xiaofei Yang, Dong Tang, Yiru Dong, Li Zhang, and Weixin Xie. "Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification." Remote Sensing 16, no. 22 (2024): 4149. http://dx.doi.org/10.3390/rs16224149.

Full text

Abstract:

Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to model an HSI, which means that spatial and spectral information are treated equally in the feature-modeling process. However, spectral information is considered to be more domain-invariant than spatial information. Treating the spatial and spectral information equally may result in parameter redundan

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Bingxin, and Dehong Yu. "A Divide-and-Conquer Strategy for Cross-Domain Few-Shot Learning." Electronics 14, no. 3 (2025): 418. https://doi.org/10.3390/electronics14030418.

Full text

Abstract:

Cross-Domain Few-Shot Learning (CD-FSL) aims to empower machines with the capability to rapidly acquire new concepts across domains using an extremely limited number of training samples from the target domain. This ability hinges on the model’s capacity to extract and transfer generalizable knowledge from a source training set. Studies have indicated that the similarity between source and target-data distributions, as well as the difficulty of target tasks, determine the classification performance of the model. However, the current lack of quantitative metrics hampers researchers’ ability to d

APA, Harvard, Vancouver, ISO, and other styles

14

Guo, Qianyu, Gong Haotong, Xujun Wei, et al. "RankDNN: Learning to Rank for Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 728–36. http://dx.doi.org/10.1609/aaai.v37i1.25150.

Full text

Abstract:

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankML

APA, Harvard, Vancouver, ISO, and other styles

15

Guan, Lei, Fan Liu, Ru Zhang, Jianyi Liu, and Yifan Tang. "MCW: A Generalizable Deepfake Detection Method for Few-Shot Learning." Sensors 23, no. 21 (2023): 8763. http://dx.doi.org/10.3390/s23218763.

Full text

Abstract:

With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performan

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Xiang, Hui Luo, Gaofan Zhou, et al. "Learning general features to bridge the cross-domain gaps in few-shot learning." Knowledge-Based Systems 299 (September 2024): 112024. http://dx.doi.org/10.1016/j.knosys.2024.112024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Rostami, Mohammad, Soheil Kolouri, Eric Eaton, and Kyungnam Kim. "Deep Transfer Learning for Few-Shot SAR Image Classification." Remote Sensing 11, no. 11 (2019): 1374. http://dx.doi.org/10.3390/rs11111374.

Full text

Abstract:

The reemergence of Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a

APA, Harvard, Vancouver, ISO, and other styles

18

Xu, Yanbing, Yanmei Zhang, Tingxuan Yue, Chengcheng Yu, and Huan Li. "Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification." Remote Sensing 15, no. 4 (2023): 1125. http://dx.doi.org/10.3390/rs15041125.

Full text

Abstract:

Due to a lack of labeled samples, deep learning methods generally tend to have poor classification performance in practical applications. Few-shot learning (FSL), as an emerging learning paradigm, has been widely utilized in hyperspectral image (HSI) classification with limited labeled samples. However, the existing FSL methods generally ignore the domain shift problem in cross-domain scenes and rarely explore the associations between samples in the source and target domain. To tackle the above issues, a graph-based domain adaptation FSL (GDAFSL) method is proposed for HSI classification with

APA, Harvard, Vancouver, ISO, and other styles

19

Fayjie, Abdur R., Mathijs Lens, and Patrick Vandewalle. "Few-Shot Segmentation of 3D Point Clouds Under Real-World Distributional Shifts in Railroad Infrastructure." Sensors 25, no. 4 (2025): 1072. https://doi.org/10.3390/s25041072.

Full text

Abstract:

Industrial railway monitoring systems require precise understanding of 3D scenes, typically achieved using deep learning models for 3D point cloud segmentation. However, real-world applications demand these models to rapidly adapt to infrastructure upgrades and diverse environmental conditions across regions. Conventional deep learning models, which rely on large-scale annotated datasets for training and are evaluated on test sets that are drawn independently and identically from the training distribution, often fail to account for such real-world changes, leading to overestimated model perfor

APA, Harvard, Vancouver, ISO, and other styles

20

Jiang, Zuo, Yuan Wang, and Yi Tang. "Few-Shot Classification Based on Sparse Dictionary Meta-Learning." Mathematics 12, no. 19 (2024): 2992. http://dx.doi.org/10.3390/math12192992.

Full text

Abstract:

In the field of Meta-Learning, traditional methods for addressing few-shot learning problems often rely on leveraging prior knowledge for rapid adaptation. However, when faced with insufficient data, meta-learning models frequently encounter challenges such as overfitting and limited feature extraction capabilities. To overcome these challenges, an innovative meta-learning approach based on Sparse Dictionary and Consistency Learning (SDCL) is proposed. The distinctive feature of SDCL is the integration of sparse representation and consistency regularization, designed to acquire both broadly ap

APA, Harvard, Vancouver, ISO, and other styles

21

Li, Wenqian, Pengfei Fang, and Hui Xue. "SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15275–83. https://doi.org/10.1609/aaai.v39i15.33676.

Full text

Abstract:

Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from seen source domains to unseen target domains, which is crucial for evaluating the generalization and robustness of models. Recent studies focus on utilizing visual styles to bridge the domain gap between different domains. However, the serious dilemma of gradient instability and local optimization problem occurs in those style-based CD-FSL methods. This paper addresses these issues and proposes a novel crop-global style perturbation method, called Self-Versatility Adversarial Style Perturbation (SVasP), which enhances the

APA, Harvard, Vancouver, ISO, and other styles

22

Ma, Ran, Yixiong Zou, Yuhua Li, and Ruixuan Li. "Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19305–13. https://doi.org/10.1609/aaai.v39i18.34125.

Full text

Abstract:

Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the large domain gap makes CDFSL a challenging problem. Masked Autoencoder (MAE) excels in effectively using unlabeled data and learning image’s global structures, enhancing model generalization and robustness. However, in the CDFSL task with significant domain shifts, we find MAE even shows lower performance than the baseline supervised models. In this paper, we first delve into this phenomenon for an interpretation. We f

APA, Harvard, Vancouver, ISO, and other styles

23

Jiang, Fan, Tom Drummond, and Trevor Cohn. "Few-Shot Multilingual Open-Domain QA from Five Examples." Transactions of the Association for Computational Linguistics 13 (2025): 481–504. https://doi.org/10.1162/tacl_a_00750.

Full text

Abstract:

Abstract Recent approaches to multilingual open- domain question answering (MLODQA) have achieved promising results given abundant language-specific training data. However, the considerable annotation cost limits the application of these methods for underrepresented languages. We introduce a few-shot learning approach to synthesize large-scale multilingual data from large language models (LLMs). Our method begins with large-scale self-supervised pre-training using WikiData, followed by training on high-quality synthetic multilingual data generated by prompting LLMs with few-shot supervision. T

APA, Harvard, Vancouver, ISO, and other styles

24

Li, Binquan, Yuan Yao, and Qiao Wang. "Domain Adaptive Few-Shot Learning for ISAR Aircraft Recognition with Transferred Attention and Weighting Importance." Electronics 12, no. 13 (2023): 2909. http://dx.doi.org/10.3390/electronics12132909.

Full text

Abstract:

With the enhancement of air-based and space-based perception capabilities, space-aeronautics incorporation and integration is growing in importance. Full domain awareness is crucial for integrated perception systems, in which domain adaptation is one of the key problems in improving the performance of cross-domain perception. Deep learning is currently an advanced technique for complex inverse synthetic aperture radar (ISAR) object recognition. However, the training procedure needs many annotated samples, which is insufficient for certain targets, such as aircraft. Few-shot learning provides a

APA, Harvard, Vancouver, ISO, and other styles

25

Yang, YongJin, Taehyeon Kim, and Se-Young Yun. "Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16370–78. http://dx.doi.org/10.1609/aaai.v38i15.29573.

Full text

Abstract:

Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from various domains with only a few samples at hand. While prior approaches have primarily focused on parameter-efficient methods of using adapters, they often overlook two critical issues: shifts in batch statistics and noisy sample statistics arising from domain discrepancy variations. In this paper, we introduce Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation (ProLAD), marking two principal contributions. F

APA, Harvard, Vancouver, ISO, and other styles

26

Lin, Hong, Rita Tse, Su-Kit Tang, Zhenping Qiang, and Giovanni Pau. "Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain." Plants 11, no. 21 (2022): 2814. http://dx.doi.org/10.3390/plants11212814.

Full text

Abstract:

Few-shot learning (FSL) is suitable for plant-disease recognition due to the shortage of data. However, the limitations of feature representation and the demanding generalization requirements are still pressing issues that need to be addressed. The recent studies reveal that the frequency representation contains rich patterns for image understanding. Given that most existing studies based on image classification have been conducted in the spatial domain, we introduce frequency representation into the FSL paradigm for plant-disease recognition. A discrete cosine transform module is designed for

APA, Harvard, Vancouver, ISO, and other styles

27

Ahn, Youngdo, Sung Joo Lee, and Jong Won Shin. "Cross-Corpus Speech Emotion Recognition Based on Few-Shot Learning and Domain Adaptation." IEEE Signal Processing Letters 28 (2021): 1190–94. http://dx.doi.org/10.1109/lsp.2021.3086395.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Wang, Huaqing, Dongrui Lv, Tianjiao Lin, Changkun Han, and Liuyang Song. "Task-adaptive unbiased regularization meta-learning for few-shot cross-domain fault diagnosis." Engineering Applications of Artificial Intelligence 144 (March 2025): 110200. https://doi.org/10.1016/j.engappai.2025.110200.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Bo, Yuntian, Yazhou Zhu, Lunbo Li, and Haofeng Zhang. "FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1889–97. https://doi.org/10.1609/aaai.v39i2.32184.

Full text

Abstract:

Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which limits the applicability to current FSMIS tasks. To overcome this limitation, we focus on the cross-domain few-shot medical image segmentation (CD-FSMIS) task, aiming to develop a generalized model capable of adapting to a broader range of medical image segmentation scenarios with limited labeled data from the novel target domain. Inspired by the characteristics of frequency domain similarity across different domains, w

APA, Harvard, Vancouver, ISO, and other styles

30

Gao, Jiuyang, Siyu Li, Wenfeng Xia, Jiuyang Yu, and Yaonan Dai. "Research on a Cross-Domain Few-Shot Adaptive Classification Algorithm Based on Knowledge Distillation Technology." Sensors 24, no. 6 (2024): 1939. http://dx.doi.org/10.3390/s24061939.

Full text

Abstract:

With the development of deep learning and sensors and sensor collection methods, computer vision inspection technology has developed rapidly. The deep-learning-based classification algorithm requires the acquisition of a model with superior generalization capabilities through the utilization of a substantial quantity of training samples. However, due to issues such as privacy, annotation costs, and sensor-captured images, how to make full use of limited samples has become a major challenge for practical training and deployment. Furthermore, when simulating models and transferring them to actua

APA, Harvard, Vancouver, ISO, and other styles

31

Peng, Shi-Feng, Guolei Sun, Yong Li, Hongsong Wang, and Guo-Sen Xie. "SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6488–96. https://doi.org/10.1609/aaai.v39i6.32695.

Full text

Abstract:

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability. In this work, we propose a SAM-aware graph prompt reasoning network (GPRN) that fully leverages SAM

APA, Harvard, Vancouver, ISO, and other styles

32

Yu, Lei, Wanqi Yang, Shengqi Huang, Lei Wang, and Ming Yang. "High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11025–33. http://dx.doi.org/10.1609/aaai.v37i9.26306.

Full text

Abstract:

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim

APA, Harvard, Vancouver, ISO, and other styles

33

Zhao, Lifan, Yunlong Meng, and Lin Xu. "OA-FSUI2IT: A Novel Few-Shot Cross Domain Object Detection Framework with Object-Aware Few-Shot Unsupervised Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3426–35. http://dx.doi.org/10.1609/aaai.v36i3.20253.

Full text

Abstract:

Unsupervised image-to-image (UI2I) translation methods aim to learn a mapping between different visual domains with well-preserved content and consistent structure. It has been proven that the generated images are quite useful for enhancing the performance of computer vision tasks like object detection in a different domain with distribution discrepancies. Current methods require large amounts of images in both source and target domains for successful translation. However, data collection and annotations in many scenarios are infeasible or even impossible. In this paper, we propose an Object-A

APA, Harvard, Vancouver, ISO, and other styles

34

Xu, Congyuan, Donghui Li, Zihao Liu, Jun Yang, Qinfeng Shen, and Ningbing Tong. "Few-shot network intrusion detection method based on multi-domain fusion and cross-attention." PLOS One 20, no. 7 (2025): e0327161. https://doi.org/10.1371/journal.pone.0327161.

Full text

Abstract:

Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. To address this challenge, we propose a novel few-shot intrusion detection method that integrates multi-domain feature fusion with a bidirectional cross-attention mechanism. Specifically, the method adopts a dual-branch feature extractor to jointly capture spatial and frequency domain characteristics of network traffic. The frequency domain features are o

APA, Harvard, Vancouver, ISO, and other styles

35

Hu, Junwei, Weigang Li, Yong Zhang, and Zhiqiang Tian. "Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network." Engineering Applications of Artificial Intelligence 136 (October 2024): 108970. http://dx.doi.org/10.1016/j.engappai.2024.108970.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Niu, Simin, Xun Liang, Sensen Zhang, Shichao Song, Xuan Zhang, and Xiaoping Zhou. "When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23600–23601. http://dx.doi.org/10.1609/aaai.v38i21.30489.

Full text

Abstract:

Cross-domain Graph Meta-learning (CGML) has shown its promise, where meta-knowledge is extracted from few-shot graph data in multiple relevant but distinct domains. However, several recent efforts assume target data available, which commonly does not established in practice. In this paper, we devise a novel Cross-domain Data Augmentation for Graph Meta-Learning (CDA-GML), which incorporates the superiorities of CGML and Data Augmentation， has addressed intractable shortcomings of label sparsity, domain shift, and the absence of target data simultaneously. Specifically, our method simulates ins

APA, Harvard, Vancouver, ISO, and other styles

37

Hu, Pengli, Chengpei Tang, Kang Yin, and Xie Zhang. "WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network." Applied Sciences 11, no. 8 (2021): 3329. http://dx.doi.org/10.3390/app11083329.

Full text

Abstract:

Wi-Fi sensing technology based on deep learning has contributed many breakthroughs in gesture recognition tasks. However, most methods concentrate on single domain recognition with high computational complexity while rarely investigating cross-domain recognition with lightweight performance, which cannot meet the requirements of high recognition performance and low computational complexity in an actual gesture recognition system. Inspired by the few-shot learning methods, we propose WiGR, a Wi-Fi-based gesture recognition system. The key structure of WiGR is a lightweight few-shot learning net

APA, Harvard, Vancouver, ISO, and other styles

38

Li, Pengfang, Fang Liu, Licheng Jiao, et al. "Knowledge Transduction for Cross-Domain Few-Shot Learning." Pattern Recognition, April 2023, 109652. http://dx.doi.org/10.1016/j.patcog.2023.109652.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Ji, Zhong, Xiangyu Kong, Xuan Wang, and Xiyao Liu. "Relevance equilibrium network for cross-domain few-shot learning." International Journal of Multimedia Information Retrieval 13, no. 2 (2024). http://dx.doi.org/10.1007/s13735-024-00333-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Hongyu, Eibe Frank, Bernhard Pfahringer, Michael Mayo, and Geoffrey Holmes. "Feature extractor stacking for cross-domain few-shot learning." Machine Learning, November 30, 2023. http://dx.doi.org/10.1007/s10994-023-06483-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Zhuo, Linhai, Yuqian Fu, Jingjing Chen, Yixin Cao, and Yu-Gang Jiang. "Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning." ACM Transactions on Multimedia Computing, Communications, and Applications, June 19, 2024. http://dx.doi.org/10.1145/3673231.

Full text

Abstract:

The challenge of cross-domain few-shot learning (CD-FSL) stems from the substantial distribution disparities between target and source domain images, necessitating a model with robust generalization capabilities. In this work, we posit that large-scale pretrained models are pivotal in addressing the cross-domain few-shot learning task owing to their exceptional representational and generalization prowess. To our knowledge, no existing research comprehensively investigates the utility of large-scale pretrained models in the cross-domain few-shot learning context. Addressing this gap, our study

APA, Harvard, Vancouver, ISO, and other styles

42

Chang, Xinyi, Chunyu Du, Xinjing Song, Weifeng Liu, and Yanjiang Wang. "Target Oriented Dynamic Adaption for Cross-Domain Few-Shot Learning." Neural Processing Letters 56, no. 3 (2024). http://dx.doi.org/10.1007/s11063-024-11508-0.

Full text

Abstract:

AbstractFew-shot learning has achieved satisfactory progress over the years, but these methods implicitly hypothesize that the data in the base (source) classes and novel (target) classes are sampled from the same data distribution (domain), which is often invalid in reality. The purpose of cross-domain few-shot learning (CD-FSL) is to successfully identify novel target classes with a small quantity of labeled instances on the target domain under the circumstance of domain shift between the source domain and the target domain. However, in CD-FSL, the knowledge learned by the network on the sou

APA, Harvard, Vancouver, ISO, and other styles

43

Gong, Yuxuan, Yuqi Yue, Weidong Ji, and Guohui Zhou. "Cross-domain few-shot learning based on pseudo-Siamese neural network." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-28588-y.

Full text

Abstract:

AbstractCross-domain few-shot learning is one of the research highlights in machine learning. The difficulty lies in the accuracy drop of cross-domain network learning on a single domain due to the differences between the domains. To alleviate the problem, according to the idea of contour cognition and the process of human recognition, we propose a few-shot learning method based on pseudo-Siamese convolution neural network. The original image and the sketch map are respectively sent to the branch network in the pre-training and meta-learning process. While maintaining the original image featur

APA, Harvard, Vancouver, ISO, and other styles

44

Wang, Hongyu, Henry Gouk, Huon Fraser, et al. "Experiments in Cross-Domain Few-Shot Learning for Image Classification Reproducibility Package." September 17, 2021. https://doi.org/10.5281/zenodo.5152448.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Ye, Zhen, Jie Wang, Huan Liu, Yu Zhang, and Wei Li. "Adaptive Domain-Adversarial Few-Shot Learning for Cross-Domain Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing, 2023, 1. http://dx.doi.org/10.1109/tgrs.2023.3334289.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Li, Zhaokui, Ming Liu, Yushi Chen, Yimin Xu, Wei Li, and Qian Du. "Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing, 2021, 1–18. http://dx.doi.org/10.1109/tgrs.2021.3057066.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Hongyu, Henry Gouk, Huon Fraser, et al. "Experiments in cross-domain few-shot learning for image classification." Journal of the Royal Society of New Zealand, April 7, 2022, 1–23. http://dx.doi.org/10.1080/03036758.2022.2059767.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Hui, Siqi, Sanping Zhou, Ye Deng, Yang Wu, and Jinjun Wang. "Gradient-guided channel masking for cross-domain few-shot learning." Knowledge-Based Systems, October 2024, 112548. http://dx.doi.org/10.1016/j.knosys.2024.112548.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Liu, Yicong, Yixiong Zou, Ruixuan Li, and Yuhua Li. "Spectral Decomposition and Transformation for Cross-domain few-shot Learning." Neural Networks, July 2024, 106536. http://dx.doi.org/10.1016/j.neunet.2024.106536.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Zhou, Fei, Peng Wang, Lei Zhang, Wei Wei, and Yanning Zhang. "Meta-collaborative comparison for effective cross-domain few-shot learning." Pattern Recognition, July 2024, 110790. http://dx.doi.org/10.1016/j.patcog.2024.110790.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!