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 https://github.com/ZJULearning/PTL

本研究提出了一种基于 Batch-related Convolutional Cell (BConv-Cell) 的 Progressive Transfer Learning (PTL) 方法，通过收集数据集的全局信息，优化预训练的 ReID 模型，显著提高 MSMT17、Market-1501、CUHK03 和 DukeMTMC-reID 数据集上的性能和一般图像分类基准模型的性能。

渐进式迁移学习

Progressive Transfer Learning

Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy.

本文提出了一种基于深度卷积神经网络的学习系统，该系统对视觉相似的类别进行聚类，并学习针对每个子集的特定深度卷积神经网络特征，在测试时不需要边界框。在流行的 Caltech-UCSD 鸟类数据集上，实验证明该方法优于最近的细粒度分类方法，无需边界框，在最困难的情况下达到了 77.5% 的平均准确率，而先前的最佳表现为 73.2%。本文还表明，渐进式迁移学习使我们能够先学习通用领域的特征，然后将其调整到特定的鸟类集合中，从而提高准确性。