Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

• URL: http://arxiv.org/abs/2207.07826v1
• Date: Sat, 16 Jul 2022 03:40:38 GMT
• Title: Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
• Authors: Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan
• Abstract summary: Few-shot learning aims to recognize novel queries with only a few support samples. We consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. We propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations.
• Score: 74.90423071048458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class. To this end, we propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations, so that the domain shift and few-shot learning can be addressed simultaneously. We evaluate our approach on two new CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets respectively. Remarkably, our approach outperforms multiple elaborated baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on average on DomainNet. Code is available at https://github.com/WentaoChen0813/CDCS-FSL

Related papers

• ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning [95.78635058475439]
  Cross-Domain Few-Shot Learning aims at addressing the Few-Shot Learning problem across different domains. This paper technically contributes a novel Multi-Expert Domain Decompositional Network (ME-D2N) We present a novel domain decomposition module that learns to decompose the student model into two domain-related sub parts.
  arXiv  Detail & Related papers  (2022-10-11T09:24:47Z)
• Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval [55.122020263319634]
  Video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain but the domain of interest only contains unannotated datasets. We propose a novel Multi-Modal Cross-Domain Alignment network to transfer the annotation knowledge from the source domain to the target domain.
  arXiv  Detail & Related papers  (2022-09-23T12:58:20Z)
• Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data [95.47859525676246]
  A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. In this paper, we realize that the labeled target data in Cross-Domain Few-Shot Learning has not been leveraged in any way to help the learning process.
  arXiv  Detail & Related papers  (2021-07-26T06:15:45Z)
• Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation [91.58443042554903]
  We propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA) PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively.
  arXiv  Detail & Related papers  (2021-03-31T02:07:42Z)
• SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning [3.6398662687367973]
  We present a novel, flexible and effective method to address the Cross-Domain Few-Shot Learning problem. Our method combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network. We observe significant improvements in accuracy across 5, 20 and 50 shot, and on the four target domains.
  arXiv  Detail & Related papers  (2020-12-03T09:29:35Z)
• Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition [31.81367604846625]
  Cross-domain few-shot learning is proposed to transfer knowledge from general-domain base classes to special-domain novel classes. In this paper, we study a challenging subset of CDFSL where the novel classes are in distant domains from base classes. We propose a residual-prediction task to encourage mid-level features to learn discriminative information of each sample.
  arXiv  Detail & Related papers  (2020-08-07T12:45:39Z)
• Domain-Adaptive Few-Shot Learning [124.51420562201407]
  We propose a novel domain-adversarial network (DAPN) model for domain-adaptive few-shot learning. Our solution is to explicitly enhance the source/target per-class separation before domain-adaptive feature embedding learning.
  arXiv  Detail & Related papers  (2020-03-19T08:31:14Z)

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.