  Domain adaptive object detection (DAOD) aims to generalize detectors trained
on an annotated source domain to an unlabelled target domain. However, existing
methods focus on reducing the domain bias of the detection backbone by
inferring a discriminative visual encoder, while ignoring the domain bias in
the detection head. Inspired by the high generalization of vision-language
models (VLMs), applying a VLM as the robust detection backbone following a
domain-aware detection head is a reasonable way to learn the discriminative
detector for each domain, rather than reducing the domain bias in traditional
methods. To achieve the above issue, we thus propose a novel DAOD framework
named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies
the learnable domain-adaptive prompt to generate the dynamic detection head for
each domain. Formally, the domain-adaptive prompt consists of the
domain-invariant tokens, domain-specific tokens, and the domain-related textual
description along with the class label. Furthermore, two constraints between
the source and target domains are applied to ensure that the domain-adaptive
prompt can capture the domains-shared and domain-specific knowledge. A prompt
ensemble strategy is also proposed to reduce the effect of prompt disturbance.
Comprehensive experiments over multiple cross-domain adaptation tasks
demonstrate that using the domain-adaptive prompt can produce an effectively
domain-related detection head for boosting domain-adaptive object detection.
Our code is available at https://github.com/Therock90421/DA-Pro.
