Zero-shot domain adaptation (ZSDA) presents substantial challenges due to the lack of images in the target domain. Previous approaches leverage Vision-Language Models (VLMs) to tackle this challenge, exploiting their zero-shot learning capabilities. However, these methods primarily address domain distribution shifts and overlook the misalignment between the detection task and VLMs, which rely on manually crafted prompts. To overcome these limitations, we propose the unified prompt and representation enhancement (UPRE) framework, which jointly optimizes both textual prompts and visual representations. Specifically, our approach introduces a multi-view domain prompt that combines linguistic domain priors with detection-specific knowledge, and a visual representation enhancement module that produces domain style variations. Furthermore, we introduce multi-level enhancement strategies, including relative domain distance and positive-negative separation, which align multi-modal representations at the image level and capture diverse visual representations at the instance level, respectively. Extensive experiments conducted on nine benchmark datasets demonstrate the superior performance of our framework in ZSDA detection scenarios. Code is available at https://github.com/AMAP-ML/UPRE.

Detecting small-rotated objects in remote sensing remains a challenging task due to feature dilution and insufficient rotation invariance. Feature dilution arises when small object features are overwhelmed by background noise and progressively lost as network depth increases. Meanwhile, the lack of rotation invariance stems from the fixed nature of convolution, which struggles to handle arbitrary orientations. To address these challenges, we propose a rotation-invariant knowledge distillation, a visual-language models (VLMs) driven knowledge distillation framework tailored for optimizing small-rotated object detection in remote sensing. Our method introduces two novel components:Enhanced-Consistency Feature Distillation(ECFD) andRotation-Invariant Feature Distillation(RIFD). ECFD mitigates feature dilution by aligning consistent language representations from VLMs with cross-depth features, ensuring consistent small-rotated object representation across different depths. RIFD enhances rotation invariance by leveraging VLMs to distill robust rotational knowledge into detectors, aligning positive and negative language features with detector features to reduce sensitivity to orientation changes and mitigate class confusion. Without introducing additional computational overhead during inference, our method significantly improves the performance of remote sensing object detectors. Extensive experiments on public remote sensing datasets with complex scenes demonstrate the state-of-the-art results. Code is available at https://github.com/Shower-Lee9527/CRKD.