Rethinking data collection for person re-identification: active redundancy reduction

作者：

Highlights：

• To address the data collection problem in person reID, we present a novel active redundancy reduction framework to alleviate the data redundancy problem in public re-ID datasets.

• To minimize the annotation workload while maximizing the performance of the re-ID model, a simple baseline is presented to select informative and diverse samples for annotation by estimating their uncertainty and intra-diversity.

• A computer-assisted Identity Recommendation Module is proposed to help the human annotators to rapidly and accurately label the selected samples.

摘要

•To address the data collection problem in person reID, we present a novel active redundancy reduction framework to alleviate the data redundancy problem in public re-ID datasets.•To minimize the annotation workload while maximizing the performance of the re-ID model, a simple baseline is presented to select informative and diverse samples for annotation by estimating their uncertainty and intra-diversity.•A computer-assisted Identity Recommendation Module is proposed to help the human annotators to rapidly and accurately label the selected samples.

论文关键词：Person re-identification,Redundancy reduction,Active learning

论文评审过程：Received 22 June 2020, Revised 27 December 2020, Accepted 2 January 2021, Available online 16 January 2021, Version of Record 21 January 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107827