Dataset Bias in Few-Shot Image Recognition
文献类型：期刊论文
| 作者 | Jiang, Shuqiang1,2; Zhu, Yaohui1,2; Liu, Chenlong1,2; Song, Xinhang1,2; Li, Xiangyang1,2; Min, Weiqing1,2 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2023 |
| 卷号 | 45期号:1页码:229-246 |
| 关键词 | Dataset bias few-shot image recognition knowledge transfer meta-learning |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2022.3153611 |
| 英文摘要 | The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such transferable capability may be impacted by the dataset bias, and this problem has rarely been investigated before. Besides, most of few-shot learning methods are biased to different datasets, which is also an important issue that needs to be investigated deeply. In this paper, we first investigate the impact of transferable capabilities learned from base categories. Specifically, we use the relevance to measure relationships between base categories and novel categories. Distributions of base categories are depicted via the instance density and category diversity. The FSIR model learns better transferable knowledge from relevant training data. In the relevant data, dense instances or diverse categories can further enrich the learned knowledge. Experimental results on different sub-datasets of Imagenet demonstrate category relevance, instance density and category diversity can depict transferable bias from distributions of base categories. Second, we investigate performance differences on different datasets from the aspects of dataset structures and different few-shot learning methods. Specifically, we introduce image complexity, intra-concept visual consistency, and inter-concept visual similarity to quantify characteristics of dataset structures. We use these quantitative characteristics and eight few-shot learning methods to analyze performance differences on multiple datasets. Based on the experimental analysis, some insightful observations are obtained from the perspective of both dataset structures and few-shot learning methods. We hope these observations are useful to guide future few-shot learning research on new datasets or tasks. Our data is available at http://123.57.42.89/dataset-bias/dataset-bias.html. |
| 资助项目 | National Natural Science Foundation of China[62125207] ; National Natural Science Foundation of China[U1936203] ; National Natural Science Foundation of China[62032022] ; National Natural Science Foundation of China[U19B2040] ; Beijing Natural Science Foundation[Z190020] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000899419900015 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/20139]  |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Jiang, Shuqiang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, CAS, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Shuqiang,Zhu, Yaohui,Liu, Chenlong,et al. Dataset Bias in Few-Shot Image Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(1):229-246. |
| APA | Jiang, Shuqiang,Zhu, Yaohui,Liu, Chenlong,Song, Xinhang,Li, Xiangyang,&Min, Weiqing.(2023).Dataset Bias in Few-Shot Image Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(1),229-246. |
| MLA | Jiang, Shuqiang,et al."Dataset Bias in Few-Shot Image Recognition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.1(2023):229-246. |
入库方式： OAI收割
来源：计算技术研究所
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