MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection

TL;DR

This paper introduces a memory-based framework for cross-domain object detection that improves instance-level feature alignment by retrieving the most similar source instances from a dynamic memory, addressing mini-batch diversity issues.

Contribution

The proposed method uses a memory module to store source features and a retrieval mechanism to enhance instance alignment across domains, outperforming existing methods.

Findings

Outperforms existing non-memory-based methods in various domain shift scenarios.

Addresses mini-batch diversity limitations in instance-level alignment.

Enhances cross-domain object detection accuracy.

Abstract

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.