  This paper studies the challenging 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, such as
DE-ViT, show promise in traditional few-shot object detection, their
generalization to CD-FSOD remains unclear: 1) can such open-set detection
methods easily generalize to CD-FSOD? 2) If not, how can models be enhanced
when facing huge domain gaps? To answer the first question, we employ measures
including style, inter-class variance (ICV), and indefinable boundaries (IB) to
understand the domain gap. Based on these measures, we establish a new
benchmark named CD-FSOD to evaluate object detection methods, revealing that
most of the current approaches fail to generalize across domains. Technically,
we observe that the performance decline is associated with our proposed
measures: style, ICV, and IB. Consequently, we propose several novel modules to
address these issues. First, the learnable instance features align initial
fixed instances with target categories, enhancing feature distinctiveness.
Second, the instance reweighting module assigns higher importance to
high-quality instances with slight IB. Third, the domain prompter encourages
features resilient to different styles by synthesizing imaginary domains
without altering semantic contents. These techniques collectively contribute to
the development of the Cross-Domain Vision Transformer for CD-FSOD (CD-ViTO),
significantly improving upon the base DE-ViT. Experimental results validate the
efficacy of our model.
