4 research outputs found
TTT-UCDR: Test-time Training for Universal Cross-Domain Retrieval
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 multiple 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 challenges associated with generalized
retrieval problems under a low-data regime, which is quite relevant in many
real-world scenarios. We attempt to make any retrieval model trained on a small
cross-domain dataset (containing just two training domains) more generalizable
towards any unknown query domain or category by quickly adapting it to the test
data during inference. This form of test-time training or adaptation of the
retrieval model is explored by means of a number of self-supervision-based loss
functions, for example, Rotnet, Jigsaw-puzzle, Barlow twins, etc., in this
work. Extensive experiments on multiple large-scale datasets demonstrate the
effectiveness of the proposed approach.Comment: 9 pages, 1 figure, 3 table