Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
- URL: http://arxiv.org/abs/2303.08557v1
- Date: Wed, 15 Mar 2023 12:18:16 GMT
- Title: Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
- Authors: Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal M. Patel, Li Liu
- Abstract summary: Cross-domain few-shot learning (CDFSL) has gained attention as it allows source and target data from different domains and label spaces.
This paper provides a comprehensive review of CDFSL at the first time, which has received far less attention than FSL due to its unique setup and difficulties.
- Score: 56.58687826135517
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep learning has been highly successful in computer vision with large
amounts of labeled data, but struggles with limited labeled training data. To
address this, Few-shot learning (FSL) is proposed, but it assumes that all
samples (including source and target task data, where target tasks are
performed with prior knowledge from source ones) are from the same domain,
which is a stringent assumption in the real world. To alleviate this
limitation, Cross-domain few-shot learning (CDFSL) has gained attention as it
allows source and target data from different domains and label spaces. This
paper provides a comprehensive review of CDFSL at the first time, which has
received far less attention than FSL due to its unique setup and difficulties.
We expect this paper to serve as both a position paper and a tutorial for those
doing research in CDFSL. This review first introduces the definition of CDFSL
and the issues involved, followed by the core scientific question and
challenge. A comprehensive review of validated CDFSL approaches from the
existing literature is then presented, along with their detailed descriptions
based on a rigorous taxonomy. Furthermore, this paper outlines and discusses
several promising directions of CDFSL that deserve further scientific
investigation, covering aspects of problem setups, applications and theories.
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