Adversarial Open-World Person Re-Identification

Adversarial Open-World Person Re-Identification is the my first paper as the first-author during my undergraduate life. The acceptance to ECCV 2018 makes it more exciting and meaningful to me.

Actually it is this work that helps me have a better understanding towards real research, including discussing about it over and over again, doing experiments over and over again and polishing it over and over again until the DDL of paper submission, which gives me the opportunity to see what’s like in Guangzhou at 4:00 am. Prof. Zheng and PhD. Wu helped me a lot, and I really appreciate it.

The paper is available at Arxiv.

How did I get this idea?

In open-world person re-identification (hereinafter called re-id) scenario, the major issue is to distinguish target people from non-target ones. And obviously non-target people who look like (or are close to in feature space) target people are a main threat to our re-id system. A simple solution is that we can find target-like non-target people in the dataset and alert the network about this by designing a loss to push them away. But you can never get enough such samples in a fixed dataset, so here comes the power of Generative Adversarial Networks (GANs) [1].

This work was initially inspired by Zheng’s paper Unlabeled samples generated by gan improve the person re-identification baseline in vitro [2] when Prof. Zheng talked with me saying that maybe we can do something with GAN in open-world person re-id. And Zheng’s work was one of the mere works about applying GAN in person re-id at that time. However Zheng’s work is very limited, or the using of GAN is very limited. Leaving apart the loss design, in my understanding, GAN in this work is just used to generate unlabeled samples to augment the dataset. They trained the GAN in the first place, and used it to get more samples to form a larger dataset. And here is where the job of GAN ends. The following processes are all about how to make use of the enlarged dataset, which contains some unlabeled samples.

But I demanded more. I was thinking about how to make GAN part of our end-to-end learning process. Cause in this way, the generated samples are not fixed but becoming more and more efficient since the weights of GAN are changing during this end-to-end learning. In this case, since we use GAN to generate adversarial samples to attack the feature extraction network, with the stronger (robuster) the feature extractor becomes, the GAN learns something new and gets stronger (can produce more efficient samples) too, thus better feature extractor can be obtained by using better imposter samples generated by GAN.

The essence of APN: weight sharing

How can we make the GAN become better with the improvement of the feature extractor? And it would be better if the GAN can always know the instant weakness of the feature extractor. By doing this, the samples generated by GAN are always targeting to attack the extractor. The answer is letting the feature extractor become part of the GAN.

In APN, we modified the GAN, and use two distinct discriminators to provide guidance to the generator, while one of the discriminator, the one used to distinguish target people from non-target ones, shares the same weights with the feature extractor. The benefit is obvious because now the generator is guided by the feature extractor’s ability of discriminating targets and non-targets. So the generated samples are very efficient and specifically targeting to attack the growing discriminating ability of feature extractor. Our goal of letting the feature extractor become robuster to similar target and non-target people is achieved. In every batch, the feature extractor becomes robuster and the generator also becomes more efficient.


In this post, I just talked about the basic idea and my perspective about our work Adversarial Open-World Person Re-Identification, which is coarse and not detailed. For more information, it’s better to explore the paper yourself. :)


  1. 1.I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio. Generative Adversarial Networks. NIPS 2014.
  2. 2.Z. Zheng, L. Zheng and Y. Yang. Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro. CoRR 2017.
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