  Image retrieval under generalized test scenarios has gained significant
momentum in literature, and the recently proposed protocol of Universal
Cross-domain Retrieval is a pioneer in this direction. A common practice in any
such generalized classification or retrieval algorithm is to exploit samples
from many domains during training to learn a domain-invariant representation of
data. Such criterion is often restrictive, and thus in this work, for the first
time, we explore the generalized retrieval problem in a data-efficient manner.
Specifically, we aim to generalize any pre-trained cross-domain retrieval
network towards any unknown query domain/category, by means of adapting the
model on the test data leveraging self-supervised learning techniques. Toward
that goal, we explored different self-supervised loss functions~(for example,
RotNet, JigSaw, Barlow Twins, etc.) and analyze their effectiveness for the
same. Extensive experiments demonstrate the proposed approach is simple, easy
to implement, and effective in handling data-efficient UCDR.
