Fine-grained image search is still a challenging problem due to the
difficulty in capturing subtle differences regardless of pose variations of
objects from fine-grained categories. In practice, a dynamic inventory with new
fine-grained categories adds another dimension to this challenge. In this work,
we propose an end-to-end network, called FGGAN, that learns discriminative
representations by implicitly learning a geometric transformation from
multi-view images for fine-grained image search. We integrate a generative
adversarial network (GAN) that can automatically handle complex view and pose
variations by converting them to a canonical view without any predefined
transformations. Moreover, in an open-set scenario, our network is able to
better match images from unseen and unknown fine-grained categories. Extensive
experiments on two public datasets and a newly collected dataset have
demonstrated the outstanding robust performance of the proposed FGGAN in both
closed-set and open-set scenarios, providing as much as 10% relative
improvement compared to baselines.

本论文提出了一种名为 FGGAN 的端到端网络，通过从多视角图像中隐式学习几何变换的方式来学习判别性表征，进而解决精细图像检索中由于姿态变化造成的难点；该网络使用生成式对抗网络（GAN）自动处理复杂视角和姿态的差异，并在开放集情况下能够更好地匹配来自未见过和未知的细粒度类别的图像，与基线相比实现了 10% 的相对改进。