  Model fine-tuning is a widely used transfer learning approach in person
Re-identification (ReID) applications, which fine-tuning a pre-trained feature
extraction model into the target scenario instead of training a model from
scratch. It is challenging due to the significant variations inside the target
scenario, e.g., different camera viewpoint, illumination changes, and
occlusion. These variations result in a gap between the distribution of each
mini-batch and the whole dataset's distribution when using mini-batch training.
In this paper, we study model fine-tuning from the perspective of the
aggregation and utilization of the global information of the dataset when using
mini-batch training. Specifically, we introduce a novel network structure
called Batch-related Convolutional Cell (BConv-Cell), which progressively
collects the global information of the dataset into a latent state and uses it
to rectify the extracted feature. Based on BConv-Cells, we further proposed the
Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning
process by jointly optimizing the BConv-Cells and the pre-trained ReID model.
Empirical experiments show that our proposal can improve the performance of the
ReID model greatly on MSMT17, Market-1501, CUHK03 and DukeMTMC-reID datasets.
Moreover, we extend our proposal to the general image classification task. The
experiments in several image classification benchmark datasets demonstrate that
our proposal can significantly improve the performance of baseline models. The
code has been released at \url{https://github.com/ZJULearning/PTL}
