Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models

2026-08-21

AI Summary

该研究针对跨域目标检测中教师-学生特征图语义不兼容问题，提出了一种基于DINOv2的语义定位增强教师模型SLE-T，通过改进特征表示和知识蒸馏方法提高了跨域识别性能。

Abstract

Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based methods overlook the spatial-scale discrepancy between teacher and student feature maps, resulting in semantic incompatibility that weakens both feature alignment and pseudo-label learning. Moreover, domain shift can cause source-trained VFM teachers to miss target-domain objects, limiting the quality of their pseudo-labels. To address these issues, we propose the Semantic Localization-Enhanced Teacher (SLE-T), a semantically compatible knowledge-distillation framework built around a lightweight SLE Adapter for DINOv2. SLE Adapter injects pretrained local-texture priors into DINOv2 to improve cross-domain recognition and reformulates its features into dense representations that are spatially and semantically compatible with the student detector. SLE-T transfers the resulting teacher knowledge through either pseudo-label learning or feature alignment. We instantiate SLE-T with DINOv2-B and DINOv2-L (the ViT-B and ViT-L variants) and compare them with the larger DINOv2-G teacher. Extensive experiments on three DAOD benchmarks demonstrate that our method achieves state-of-the-art performance, and ablation studies confirm the importance of teacher-student semantic compatibility. Notably, SLE-T with DINOv2-B produces competitive or superior pseudo-labels using approximately one-quarter of the training time of DINOv2-G and substantially less GPU memory, demonstrating efficient VFM knowledge transfer under limited computational resources.

Problem

• Vision Foundation Models
• Cross-Domain Object Detection
• Semantic Incompatibility
• Domain Shift

Innovation

• Semantic Localization-Enhanced Teacher
• SLE Adapter
• DINOv2
• Cross-Domain Object Detection
• Knowledge Distillation