This paper addresses the challenge of cross-domain few-shot object detection
(CD-FSOD), aiming to develop an accurate object detector for novel domains with
minimal labeled examples. While transformer-based open-set detectors e.g.,
DE-ViT~\cite{zhang2023detect} have excelled in both open-vocabulary object
detection and traditional few-shot object detection, detecting categories
beyond those seen during training, we thus naturally raise two key questions:
1) can such open-set detection methods easily generalize to CD-FSOD? 2) If no,
how to enhance the results of open-set methods when faced with significant
domain gaps? To address the first question, we introduce several metrics to
quantify domain variances and establish a new CD-FSOD benchmark with diverse
domain metric values. Some State-Of-The-Art (SOTA) open-set object detection
methods are evaluated on this benchmark, with evident performance degradation
observed across out-of-domain datasets. This indicates the failure of adopting
open-set detectors directly for CD-FSOD. Sequentially, to overcome the
performance degradation issue and also to answer the second proposed question,
we endeavor to enhance the vanilla DE-ViT. With several novel components
including finetuning, a learnable prototype module, and a lightweight attention
module, we present an improved Cross-Domain Vision Transformer for CD-FSOD
(CD-ViTO). Experiments show that our CD-ViTO achieves impressive results on
both out-of-domain and in-domain target datasets, establishing new SOTAs for
both CD-FSOD and FSOD. All the datasets, codes, and models will be released to
the community.

本研究致力于解决跨领域小样本目标检测（CD-FSOD）的挑战，提出了一种准确的对象检测器，用于具有最少标记示例的新领域。通过引入多个指标来量化域差异，并建立具有多样化域度量值的新的 CD-FSOD 基准，对一些最先进的开放式对象检测方法进行评估，结果表明，直接采用开放式检测器进行 CD-FSOD 会导致性能下降。“CD-ViTO” 通过引入多种新颖组件，包括微调、可学习的原型模块和轻量级注意力模块，旨在克服性能降级问题并回答提出的第二个问题。实验证明，我们的 CD-ViTO 在领域外和领域内目标数据集上均取得了令人印象深刻的结果，为 CD-FSOD 和 FSOD 建立了新的 SOTAs。所有数据集、代码和模型将向社区发布。