Semi-Supervised Domain Generalization for Object Detection via
  Language-Guided Feature Alignment
Sina Malakouti Adriana Kovashka
Existing domain adaptation (DA) and generalization (DG) methods in object
detection enforce feature alignment in the visual space but face challenges
like object appearance variability and scene complexity, which make it
difficult to distinguish between objects and achieve accurate detection. In
this paper, we are the first to address the problem of semi-supervised domain
generalization by exploring vision-language pre-training and enforcing feature
alignment through the language space. We employ a novel Cross-Domain
Descriptive Multi-Scale Learning (CDDMSL) aiming to maximize the agreement
between descriptions of an image presented with different domain-specific
characteristics in the embedding space. CDDMSL significantly outperforms
existing methods, achieving 11.7% and 7.5% improvement in DG and DA settings,
respectively. Comprehensive analysis and ablation studies confirm the
effectiveness of our method, positioning CDDMSL as a promising approach for
domain generalization in object detection tasks.