MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object
  Detection
Onkar Krishna Hiroki Ohashi Saptarshi Sinha
Cross-domain object detection is challenging, and it involves aligning
labeled source and unlabeled target domains. Previous approaches have used
adversarial training to align features at both image-level and instance-level.
At the instance level, finding a suitable source sample that aligns with a
target sample is crucial. A source sample is considered suitable if it differs
from the target sample only in domain, without differences in unimportant
characteristics such as orientation and color, which can hinder the model's
focus on aligning the domain difference. However, existing instance-level
feature alignment methods struggle to find suitable source instances because
their search scope is limited to mini-batches. Mini-batches are often so small
in size that they do not always contain suitable source instances. The
insufficient diversity of mini-batches becomes problematic particularly when
the target instances have high intra-class variance. To address this issue, we
propose a memory-based instance-level domain adaptation framework. Our method
aligns a target instance with the most similar source instance of the same
category retrieved from a memory storage. Specifically, we introduce a memory
module that dynamically stores the pooled features of all labeled source
instances, categorized by their labels. Additionally, we introduce a simple yet
effective memory retrieval module that retrieves a set of matching memory slots
for target instances. Our experiments on various domain shift scenarios
demonstrate that our approach outperforms existing non-memory-based methods
significantly.