We are thrilled to announce that our lab’s latest paper, Concept drift detection in image data stream: a survey on current literature, limitations and future directions”, has been published in the Springer Artificial Intelligence Review (IF: 14.9, Q1). Congratulate Quang-Tien! Well-done!
This paper presents the first comprehensive review of concept drift detection methods tailored for image data streams. We propose a novel taxonomy that categorizes existing approaches based on key properties such as image feature handling, detection strategy, detection level, concept drift cause, and evaluation considerations. Through the lens of this taxonomy, we analyze 14 representative concept drift detection methods designed for image data, highlighting current approaches to the field, their strengths and limitations. Based on this analysis, we outline promising future research directions to advance the field of concept drift detection in image-based systems.