Challenges

Deep re-ID models trained on the source domain may have a significant performance drop on the target domain due to the data-bias existing between source and target datasets.

-> unsupervised domain adaptation (UDA)

-> generative adversarial network (GAN)

The disparities of cameras are another critical factor influencing re-ID performance.

-> Hetero-Homogeneous Learning (HHL [1])

However, the performances of these UDA approaches are still far behind their fully-supervised counterparts. The main reason is that most previous works focus on increasing the training samples or comparing the similarity or dissimilarity between the source dataset and the target dataset but ignoring the similar natural characteristics existing in the training samples from the target domain.

Omni-Scale Feature Learning for Person Re-ID

全文链接：
https://arxiv.xilesou.top/abs/1905.00953

The source code is here.

Introduction

Major Challenges

As an instance-level recognition problem, person ReID faces two major challenges as illustrated in Figure 1 :

The intra-class (instance/identity) variations are typically big due to the changes of camera viewing conditions. - hard positives
Small inter-class variations - people in public space often wear similar clothes; from a distance as typically in surveillance videos, they can look incredibly similar. - hard negatives

Omni-Scale Featrue

To match people and distinguish them from impostors, features corresponding both small local regions and global whole body regions are important.

Looking at the global-scale features would narrow down the search to the true match (middle) and an impostor (right).
The local-scale features gives away the fact that the person on the right is an impostor. (For more challenging cases, more complicated and richer features that span multiple scales are required.)