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。

增强型开放集目标检测的跨领域少样本目标检测

Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object  Detector

Under the domain shift, cross-domain few-shot object detection aims to adapt
object detectors in the target domain with a few annotated target data. There
exists two significant challenges: (1) Highly insufficient target domain data;
(2) Potential over-adaptation and misleading caused by inappropriately
amplified target samples without any restriction. To address these challenges,
we propose an adaptive method consisting of two parts. First, we propose an
adaptive optimization strategy to select augmented data similar to target
samples rather than blindly increasing the amount. Specifically, we filter the
augmented candidates which significantly deviate from the target feature
distribution in the very beginning. Second, to further relieve the data
limitation, we propose the multi-level domain-aware data augmentation to
increase the diversity and rationality of augmented data, which exploits the
cross-image foreground-background mixture. Experiments show that the proposed
method achieves state-of-the-art performance on multiple benchmarks.

本文提出一种新方法以应对跨域少样本目标检测面临的数据不足及误导等问题，该方法由自适应优化策略与多层次领域感知数据增强两部分构成，最终在多个基准测试中取得最优表现。