While fine-grained object recognition is an important problem in computer
vision, current models are unlikely to accurately classify objects in the wild.
These fully supervised models need additional annotated images to classify
objects in every new scenario, a task that is infeasible. However, sources such
as e-commerce websites and field guides provide annotated images for many
classes. In this work, we study fine-grained domain adaptation as a step
towards overcoming the dataset shift between easily acquired annotated images
and the real world. Adaptation has not been studied in the fine-grained setting
where annotations such as attributes could be used to increase performance. Our
work uses an attribute based multi-task adaptation loss to increase accuracy
from a baseline of 4.1% to 19.1% in the semi-supervised adaptation case. Prior
do- main adaptation works have been benchmarked on small datasets such as [46]
with a total of 795 images for some domains, or simplistic datasets such as
[41] consisting of digits. We perform experiments on a subset of a new
challenging fine-grained dataset consisting of 1,095,021 images of 2, 657 car
categories drawn from e-commerce web- sites and Google Street View.

本研究探讨了细粒度领域适应的概念并提出了基于属性的多任务适应损失来解决现有细粒度目标识别模型无法准确分类野外图像的问题，通过在一组新的细粒度数据集上的实验证明了该算法可以显著提高准确性。