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Cited by 22Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

The DOI matches its Crossref record.

Incremental few-shot object detection via knowledge transfer

Feng, Hangtao, Zhang, Lu, Yang, Xu, Liu, Zhiyong·2022·Pattern Recognition Letters
DOI 10.1016/j.patrec.2022.01.024
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Crossref · 10.1016/j.patrec.2022.01.024

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Research works printed but not linked: 16

  1. Wang, 2003, Discovering the capacity of human memory, Brain Mind, 4, 189, 10.1023/A:1025405628479 · DOI
  2. Everingham, 2010, The pascal visual object classes (VOC) challenge, Int. J. Comput. Vis. (IJCV), 10.1007/s11263-009-0275-4 · DOI · Paper page
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  4. A. Bochkovskiy, C.-Y. Wang, H.-Y. M. Liao, YOLOv4: optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934 (2020). · arXiv
  5. Fu, 2019, Meta-SSD: towards fast adaptation for few-shot object detection with meta-learning, IEEE Access, 7, 77597, 10.1109/ACCESS.2019.2922438 · DOI
  6. G. Zhang, Z. Luo, K. Cui, S. Lu, Meta-DETR: few-shot object detection via unified image-level meta-learning, arXiv preprint arXiv:2103.11731 (2021). · arXiv
  7. Abraham, 2005, Memory retention–the synaptic stability versus plasticity dilemma, Trends Neurosci., 28, 73, 10.1016/j.tins.2004.12.003 · DOI · Paper page
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  9. Y. Chen, Z. Zhang, Y. Cao, L. Wang, S. Lin, H. Hu, RepPoints v2: verification meets regression for object detection, arXiv preprint arXiv:2007.08508 (2020). · arXiv
  10. Kong, 2020, FoveaBox: beyound anchor-based object detection, IEEE Trans. Image Process., 10.1109/TIP.2020.3002345 · DOI
  11. M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J.B. Tenenbaum, H. Larochelle, R.S. Zemel, Meta-learning for semi-supervised few-shot classification, arXiv preprint arXiv:1803.00676 (2018). · arXiv
  12. C. Finn, P. Abbeel, S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, arXiv preprint arXiv:1703.03400 (2017). · arXiv
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  14. Dong, 2018, Few-example object detection with model communication, IEEE Trans. Pattern Anal. Mach.Intell. (TPAMI)
  15. Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S.J. Hwang, Y. Yang, Learning to propagate labels: transductive propagation network for few-shot learning, arXiv preprint arXiv:1805.10002 (2018). · arXiv
  16. K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, et al., MMDetection: open MMLab detection toolbox and benchmark, arXiv preprint arXiv:1906.07155 (2019). · arXiv

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Metadata

Authors
Feng, H., Zhang, L., Yang, X., & Liu, Z.
Affiliations
Chinese Academy of Sciences (CN)
Corresponding author
Liu, Zhiyong
ORCID
Venue
Pattern Recognition Letters
Publisher
Elsevier BV
Published
2022-04
Volume
156
Pages
67-73
ISSN
0167-8655 (ISSN-L)
DOI
10.1016/j.patrec.2022.01.024
OpenAlex
W4210734879
Cited by
22
References
0
Citations by year
2026: 3 · 2025: 7 · 2024: 4 · 2023: 7 · 2022: 1
FWCI
1.56
Type
journal article
Language
en
Open access
closed
Licence
Locations
Pattern Recognition Letters (publishedVersion)
Funders
  • National Natural Science Foundation of China (61627808, 2019622101001)
  • National Key Research and Development Program of China (2020AAA0108902)
  • Chinese Academy of Sciences (XDB32050100)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
knowledge transfer, novel class detection, FCOS, one-stage object detection, continual learning

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