CVPR 2023 - Active Finetuning: Exploiting Annotation Budget

In this episode we discuss Active Finetuning: Exploiting Annotation Budget by Yichen Xie, Han Lu, Junchi Yan, Xiaokang Yang, Masayoshi Tomizuka, Wei Zhan. The paper proposes a new paradigm called "active finetuning" for computer vision tasks, which focuses on selecting samples for annotation in pretraining-finetuning. The proposed method, called ActiveFT, selects a subset of data that is similar in distribution to the entire unlabeled pool and maintains diversity by optimizing a parametric model in the continuous space. The experiments show that ActiveFT outperforms baselines on image classification and semantic segmentation. The code is available on GitHub.