Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection

In this paper, we focus on Single-Domain Generalized Object Detection (Single-DGOD), aiming to transfer a detector trained on one source domain to multiple unknown domains. Existing methods typically rely on simulation-driven paradigms, such as discrete augmentation or static textual prompts, to expand the boundaries of the training distribution. However, finite simulations often fail to capture the infinite dynamic variations of real-world scenarios, which can lead to overfitting on synthetic styles and limit the model's ability to handle complex structural degradations. Inspired by the manifold hypothesis, we argue that despite diverse visual variations, semantic features inherently reside on a compact, low-dimensional manifold. Thus, the key to generalization lies in learning to rectify deviant samples back onto this stable manifold, rather than merely exhausting external perturbations. To this end, we propose a new framework, i.e., Manifold Regression with Visual-Text Dual Chain-of-Thought (MR-DCoT), which reformulates robust generalization as a manifold regression problem. Specifically, we design a Visual-Text Dual Chain-of-Thought module that couples VLM-guided global semantic evolution with diffusion-based local structural perturbations to generate structured off-manifold hard examples. Subsequently, a Class-Specific Prototype Anchoring mechanism is introduced to learn a robust rectification operator that guides deviant features back toward the source semantic manifold. By establishing a closed loop of simulation for outlier generation and regression for semantic correction, our method effectively bridges the distribution gap, significantly boosting generalization and robustness to unseen shifts. Extensive evaluations on three complementary benchmarks, covering diverse driving weather conditions, real-to-art generalization, and zero-shot semantic segmentation, demonstrate the superiority and versatility of our method in handling complex domain shifts.