FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection

• URL: http://arxiv.org/abs/2511.10352v1
• Date: Fri, 14 Nov 2025 01:46:49 GMT
• Title: FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection
• Authors: Mengzhu Wang, Changyuan Deng, Shanshan Wang, Nan Yin, Long Lan, Liang Yang,
• Abstract summary: Single Domain Generalization for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains.<n>We propose a novel framework that enhances object detection by integrating the von Mises-Fisher (vMF) distribution and Fourier transformation into a CLIP-guided pipeline.<n>Our method not only preserves the semantic alignment benefits of CLIP but also enriches feature diversity and structural consistency across domains.
• Score: 46.14695068852788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent methods like CLIP-based semantic augmentation have shown promise, they often overlook the underlying structure of feature distributions and frequency-domain characteristics that are critical for robustness. In this paper, we propose a novel framework that enhances SDG object detection by integrating the von Mises-Fisher (vMF) distribution and Fourier transformation into a CLIP-guided pipeline. Specifically, we model the directional features of object representations using vMF to better capture domain-invariant semantic structures in the embedding space. Additionally, we introduce a Fourier-based augmentation strategy that perturbs amplitude and phase components to simulate domain shifts in the frequency domain, further improving feature robustness. Our method not only preserves the semantic alignment benefits of CLIP but also enriches feature diversity and structural consistency across domains. Extensive experiments on the diverse weather-driving benchmark demonstrate that our approach outperforms the existing state-of-the-art method.

Related papers

• Style-Adaptive Detection Transformer for Single-Source Domain Generalized Object Detection [7.768332621617199]
  Single-source domain generalization aims to develop a detector using only source domain data that generalizes well to unseen target domains.<n>Existing methods are primarily CNN-based and improve robustness through data augmentation combined with feature alignment.<n>We propose Style-Adaptive DEtection TRansformer (SA-DETR), a DETR-based detector tailored for single-source domain generalization.
  arXiv  Detail & Related papers  (2025-04-29T07:38:37Z)
• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• Integrating Frequency Guidance into Multi-source Domain Generalization for Bearing Fault Diagnosis [24.85752780864944]
  We propose the Fourier-based Augmentation Reconstruction Network, namely FARNet.<n>The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains.<n>To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization.
  arXiv  Detail & Related papers  (2025-02-01T20:23:03Z)
• Object Style Diffusion for Generalized Object Detection in Urban Scene [69.04189353993907]
  We introduce a novel single-domain object detection generalization method, named GoDiff.<n>By integrating pseudo-target domain data with source domain data, we diversify the training dataset.<n> Experimental results demonstrate that our method not only enhances the generalization ability of existing detectors but also functions as a plug-and-play enhancement for other single-domain generalization methods.
  arXiv  Detail & Related papers  (2024-12-18T13:03:00Z)
• Causality-inspired Latent Feature Augmentation for Single Domain Generalization [13.735443005394773]
  Single domain generalization (Single-DG) intends to develop a generalizable model with only one single training domain to perform well on other unknown target domains. Under the domain-hungry configuration, how to expand the coverage of source domain and find intrinsic causal features across different distributions is the key to enhancing the models' generalization ability. We propose a novel causality-inspired latent feature augmentation method for Single-DG by learning the meta-knowledge of feature-level transformation based on causal learning and interventions.
  arXiv  Detail & Related papers  (2024-06-10T02:42:25Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Hypernetwork-Driven Model Fusion for Federated Domain Generalization [26.492360039272942]
  Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data. We propose a robust framework, coined as hypernetwork-based Federated Fusion (hFedF), using hypernetworks for non-linear aggregation. Our method employs client-specific embeddings and gradient alignment techniques to manage domain generalization effectively.
  arXiv  Detail & Related papers  (2024-02-10T15:42:03Z)
• CLIP the Gap: A Single Domain Generalization Approach for Object Detection [60.20931827772482]
  Single Domain Generalization tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. We propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss.
  arXiv  Detail & Related papers  (2023-01-13T12:01:18Z)
• Domain Generalisation for Object Detection under Covariate and Concept Shift [10.32461766065764]
  Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features. An approach to domain generalisation for object detection is proposed, the first such approach applicable to any object detection architecture.
  arXiv  Detail & Related papers  (2022-03-10T11:14:18Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.