# Progressive Transfer Learning Source: [Progressive Transfer Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:42:59.203Z Authors: Zhengxu Yu, Dong Shen, Zhongming Jin, Jianqiang Huang, Deng Cai, Xian-Sheng Hua Published: 2019-08-07T08:52:26.000Z ## Abstract 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} ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Person Re-identification on Duke MTMC-reID (test)](): Person Re-Identification; Duke MTMC-reID (test); Rank-1: 89.36. - [Person Re-identification on Market1501 (test)](): Person Re-Identification; Market1501 (test); Rank-1 Accuracy: 94.83. - [Person Re-identification on MSMT17 (test)](): Person Re-Identification; MSMT17 (test); Rank-1 Acc: 73.12. ## Original sources and implementation [Original paper]() [PDF]()