A Survey of Deep Visual Cross-Domain Few-Shot Learning

• URL: http://arxiv.org/abs/2303.09253v1
• Date: Thu, 16 Mar 2023 12:06:59 GMT
• Title: A Survey of Deep Visual Cross-Domain Few-Shot Learning
• Authors: Wenjian Wang, Lijuan Duan, Yuxi Wang, Junsong Fan, Zhi Gong, Zhaoxiang Zhang
• Abstract summary: Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. We provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view.
• Score: 46.08156372869305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have the same data distribution, this is often not the case in real-world applications. This leads to decreased model transfer effects when the new class distribution differs significantly from the learned classes. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. In this survey, we provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view. We summarise the existing CDFS network architectures and discuss the solution ideas for each direction the taxonomy indicates. Furthermore, we introduce various CDFS downstream applications and outline classification, detection, and segmentation benchmarks and corresponding standards for evaluation. We also discuss the challenges of CDFS research and explore potential directions for future investigation. Through this review, we aim to provide comprehensive guidance on CDFS research, enabling researchers to gain insight into the state-of-the-art while allowing them to build upon existing solutions to develop their own CDFS models.

Related papers

• A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research [5.08731160761218]
  Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups. Federated learning amplifies the need for fairness due to the heterogeneous data distributions across clients. No dedicated survey has focused comprehensively on group fairness in federated learning. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies.
  arXiv  Detail & Related papers  (2024-10-04T18:39:28Z)
• Unsupervised Learning for Topological Classification of Transportation Networks [3.1675545188012078]
  We present a comprehensive framework for evaluating various topological network characteristics. We employ two clustering algorithms, K-means and HDBSCAN, to classify 14 transportation networks. The PCA method, followed by the K-means clustering approach, outperforms other alternatives with a Silhouette score of $0.510$.
  arXiv  Detail & Related papers  (2023-11-23T10:18:21Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Federated Domain Generalization: A Survey [12.84261944926547]
  In machine learning, data is often distributed across different devices, organizations, or edge nodes. In response to this challenge, there has been a surge of interest in federated domain generalization. This paper presents the first survey of recent advances in this area.
  arXiv  Detail & Related papers  (2023-06-02T07:55:42Z)
• A Comprehensive Survey on Source-free Domain Adaptation [69.17622123344327]
  The research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years. We provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme. We compare the results of more than 30 representative SFDA methods on three popular classification benchmarks.
  arXiv  Detail & Related papers  (2023-02-23T06:32:09Z)
• FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification [39.01765909445661]
  We propose Born-Again Network (BAN) episodic training and comprehensively investigate its effectiveness for DG-FSC. Building on the encouraging findings, our second (major) contribution is to propose Few-Shot BAN (FS-BAN), a novel BAN approach for DG-FSC. Our proposed FS-BAN includes novel multi-task learning objectives: Mutual Regularization, Mismatched Teacher, and Meta-Control Temperature.
  arXiv  Detail & Related papers  (2022-08-23T13:02:14Z)
• Tackling Long-Tailed Category Distribution Under Domain Shifts [50.21255304847395]
  Existing approaches cannot handle the scenario where both issues exist. We designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
  arXiv  Detail & Related papers  (2022-07-20T19:07:46Z)
• Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison [54.357707168883024]
  Few-Shot Object Detection (FSOD) mimics the humans' ability of learning to learn. FSOD intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. We give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols.
  arXiv  Detail & Related papers  (2022-03-27T04:11:28Z)
• Exploring Neural Models for Query-Focused Summarization [74.41256438059256]
  We conduct a systematic exploration of neural approaches to query-focused summarization (QFS) We present two model extensions that achieve state-of-the-art performance on the QMSum dataset by a margin of up to 3.38 ROUGE-1, 3.72 ROUGE-2, and 3.28 ROUGE-L.
  arXiv  Detail & Related papers  (2021-12-14T18:33:29Z)
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study [1.2345552555178128]
  We construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. To emphasize the importance and importance of UDTL-based IFD, the whole test framework will be released to the research community.
  arXiv  Detail & Related papers  (2019-12-28T21:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.