Image search engines enable the retrieval of images relevant to a query
image. In this work, we consider the setting where a query for similar images
is derived from a collection of images. For visual search, the similarity
measurements may be made along multiple axes, or views, such as style and
color. We assume access to a set of feature extractors, each of which computes
representations for a specific view. Our objective is to design a retrieval
algorithm that effectively combines similarities computed over representations
from multiple views. To this end, we propose a self-supervised learning method
for extracting disentangled view-specific representations for images such that
the inter-view overlap is minimized. We show how this allows us to compute the
intent of a collection as a distribution over views. We show how effective
retrieval can be performed by prioritizing candidate expansion images that
match the intent of a query collection. Finally, we present a new querying
mechanism for image search enabled by composing multiple collections and
perform retrieval under this setting using the techniques presented in this
paper.

本文介绍了一种自我监督学习的方法，用于提取图像的特征向量，并将其组合用于图像搜索引擎的检索算法中。这种方法能有效地减少特征重叠，提高查询的精确性。