Skip to main content
placeholder image

Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective

Journal Article


Abstract


  • This article takes a problem-oriented perspective and presents a comprehensive review of transfer-learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into 17 problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer-learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition but also the problems (e.g., 8 of the 17 problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers but also a systematic approach and a reference for a machine-learning practitioner to categorise a real problem and to look up for a possible solution accordingly.

Publication Date


  • 2019

Citation


  • Zhang, J., Li, W., Ogunbona, P. & Xu, D. (2019). Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective. ACM Computing Surveys, 52 (1), 7-1-7-38.

Scopus Eid


  • 2-s2.0-85062460059

Start Page


  • 7-1

End Page


  • 7-38

Volume


  • 52

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • This article takes a problem-oriented perspective and presents a comprehensive review of transfer-learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into 17 problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer-learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition but also the problems (e.g., 8 of the 17 problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers but also a systematic approach and a reference for a machine-learning practitioner to categorise a real problem and to look up for a possible solution accordingly.

Publication Date


  • 2019

Citation


  • Zhang, J., Li, W., Ogunbona, P. & Xu, D. (2019). Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective. ACM Computing Surveys, 52 (1), 7-1-7-38.

Scopus Eid


  • 2-s2.0-85062460059

Start Page


  • 7-1

End Page


  • 7-38

Volume


  • 52

Issue


  • 1

Place Of Publication


  • United States