Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains

• URL: http://arxiv.org/abs/2112.13539v1
• Date: Mon, 27 Dec 2021 06:54:11 GMT
• Title: Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
• Authors: Yuan-Chia Cheng, Ci-Siang Lin, Fu-En Yang, Yu-Chiang Frank Wang
• Abstract summary: Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. In this paper, we present a unique learning framework for domain-generalized few-shot classification. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features.
• Score: 36.98387822136687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification.

Related papers

• Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification [71.08024880298613]
  We study the multi-source Domain Generalization of text classification. We propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
  arXiv  Detail & Related papers  (2024-09-20T07:46:21Z)
• CDFSL-V: Cross-Domain Few-Shot Learning for Videos [58.37446811360741]
  Few-shot video action recognition is an effective approach to recognizing new categories with only a few labeled examples. Existing methods in video action recognition rely on large labeled datasets from the same domain. We propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning.
  arXiv  Detail & Related papers  (2023-09-07T19:44:27Z)
• Few-Shot Object Detection in Unseen Domains [4.36080478413575]
  Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data. We propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. Our experiments on the T-LESS dataset show that the proposed approach succeeds in alleviating the domain gap considerably.
  arXiv  Detail & Related papers  (2022-04-11T13:16:41Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Universal Representation Learning from Multiple Domains for Few-shot Classification [41.821234589075445]
  We propose to learn a single set of universal deep representations by distilling knowledge of multiple separately trained networks. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step.
  arXiv  Detail & Related papers  (2021-03-25T13:49:12Z)
• Robust Domain-Free Domain Generalization with Class-aware Alignment [4.442096198968069]
  Domain-Free Domain Generalization (DFDG) is a model-agnostic method to achieve better generalization performance on the unseen test domain. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It obtains competitive performance on both time series sensor and image classification public datasets.
  arXiv  Detail & Related papers  (2021-02-17T17:46:06Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)
• Multi-source Domain Adaptation for Visual Sentiment Classification [92.53780541232773]
  We propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN) To handle data from multiple source domains, MSGAN learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
  arXiv  Detail & Related papers  (2020-01-12T08:37:42Z)

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.