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.

Large-scale high-resolution remote sensing images (LSHR) are increasingly adopted for object detection, since they capture finer details. However, LSHR imposes a substantial computational cost. Existing methods explore lightweight backbones and advanced oriented bounding box regression mechanisms. Nevertheless, they still rely on high-resolution inputs to maintain detection accuracy. We observe that LSHR comprise extensive background areas that can be compressed to reduce unnecessary computation, while object regions contain details that can be reserved to improve detection accuracy. Thus, we propose a hybrid Gaussian deformation module that dynamically adjusts the sampling density at each location based on its relevance to the detection task, i.e., high-density sampling preserves more object regions and better retains detailed features, while low-density sampling diminishes the background proportion. Further, we introduce a bilateral deform-uniform detection framework to exploit the potential of the deformed sampled low-resolution images and original high-resolution images. Specifically, a deformed deep backbone takes the deformed sampled images as inputs to produce high-level semantic information, and a uniform shallow backbone takes the original high-resolution images as inputs to generate precise spatial location information. Moreover, we incorporate a deformation-aware feature registration module that calibrates the spatial information of deformed features, preventing regression degenerate solutions while maintaining feature activation. Subsequently, we introduce a feature relationship interaction fusion module to balance the contributions of features from both deformed and uniform backbones. Comprehensive experiments on three challenging datasets show that our method achieves superior performance compared with the state-of-the-art methods.

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.