Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization

• URL: http://arxiv.org/abs/2604.26820v1
• Date: Wed, 29 Apr 2026 15:48:00 GMT
• Title: Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization
• Abstract summary: This paper proposes a novel Basis-driven framework for domain generalization, namely textbftextitBridge.<n>By learning the low-rank bases for front-door adjustment, textbftextitBridge blocks confounders' effects to spurious correlations.<n>textbftextitBridge can be seamlessly integrated with both discriminative (e.g., DINOv2/3, SAM) and generative (e.g., Stable Diffusion) Vision Foundation Models.
• Score: 12.808064959739655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, and style) from the source domain, leading to spurious correlations that hinder generalization. To this end, this paper proposes a novel Basis-driven framework for domain generalization, namely \textbf{\textit{Bridge}}, that incorporates causal inference into object detection. By learning the low-rank bases for front-door adjustment, \textbf{\textit{Bridge}} blocks confounders' effects to mitigate spurious correlations, while simultaneously refining representations by filtering redundant and task-irrelevant components. \textbf{\textit{Bridge}} can be seamlessly integrated with both discriminative (e.g., DINOv2/3, SAM) and generative (e.g., Stable Diffusion) Vision Foundation Models (VFMs). Extensive experiments across multiple domain generalization object detection datasets, i.e., Cross-Camera, Adverse Weather, Real-to-Artistic, Diverse Weather Datasets, and Diverse Weather DroneVehicle (our newly augmented real-world UAV-based benchmark), underscore the superiority of our proposed method over previous state-of-the-art approaches. The project page is available at: https://mingbohong.github.io/Bridge/.

Related papers

• RT-SDGOD: Real-Time Single-Domain Generalized Object Detection [29.73970506841414]
  Under real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors.<n>We introduce Real-Time Single-Domain Generalized Object Detection (RT-SDGOD)<n>Our method achieves better generalization performance than existing approaches across multiple unseen target domains.
  arXiv  Detail & Related papers  (2026-06-08T11:39:18Z)
• Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain [26.03122882760887]
  We propose a spatial-spectral collaborative perception network for cross-domain IRSTD.<n>We develop a phase rectification module (PRM) to derive generalizable target awareness.<n>We employ an attention mechanism (OAM) in skip connections to preserve positional information.
  arXiv  Detail & Related papers  (2026-04-02T11:54:28Z)
• DiveUp: Learning Feature Upsampling from Diverse Vision Foundation Models [11.659303031631723]
  DiveUp is a novel framework that breaks away from single-model dependency by introducing multi-VFM relational guidance.<n>We propose a universal relational feature representation, formulated as a local center-of-mass (COM) field, that extracts intrinsic geometric structures.<n>DiveUp is a unified, encoder-agnostic framework; a jointly-trained model can universally upsample features from diverse VFMs.
  arXiv  Detail & Related papers  (2026-03-13T20:20:22Z)
• Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts [37.886574666175065]
  Single-source Domain Generalized Object Detection is a cutting-edge research topic in computer vision.<n>Causal Visual Prompts method mitigates bias from spurious features by integrating visual prompts with cross-attention.<n>Causal achieves state-of-the-art performance with 15.9-31.4% gains over existing domain generalization methods.
  arXiv  Detail & Related papers  (2025-10-22T11:24:52Z)
• Unified Unsupervised Anomaly Detection via Matching Cost Filtering [113.43366521994396]
  Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data.<n>We present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model.
  arXiv  Detail & Related papers  (2025-10-03T03:28:18Z)
• LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation [54.690154688667086]
  Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains.<n>We propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation.
  arXiv  Detail & Related papers  (2025-09-10T14:23:07Z)
• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather [55.95708988160047]
  LiDAR semantic segmentation (LSS) is a critical task in autonomous driving. Prior LSS methods are investigated and evaluated on datasets within the same domain in clear weather. We propose UniMix, a universal method that enhances the adaptability and generalizability of LSS models.
  arXiv  Detail & Related papers  (2024-04-08T02:02:15Z)
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector [72.05791402494727]
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD) It aims to develop an accurate object detector for novel domains with minimal labeled examples.
  arXiv  Detail & Related papers  (2024-02-05T15:25:32Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Domain-aware Triplet loss in Domain Generalization [0.0]
  Domain shift is caused by discrepancies in the distributions of the testing and training data. We design a domainaware triplet loss for domain generalization to help the model to cluster similar semantic features. Our algorithm is designed to disperse domain information in the embedding space.
  arXiv  Detail & Related papers  (2023-03-01T14:02:01Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Grounding Visual Representations with Texts for Domain Generalization [9.554646174100123]
  Cross-modality supervision can be successfully used to ground domain-invariant visual representations. Our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets.
  arXiv  Detail & Related papers  (2022-07-21T03:43:38Z)
• Low-confidence Samples Matter for Domain Adaptation [47.552605279925736]
  Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain. We propose a novel contrastive learning method by processing low-confidence samples. We evaluate the proposed method in both unsupervised and semi-supervised DA settings.
  arXiv  Detail & Related papers  (2022-02-06T15:45:45Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• Anomaly Detection with Domain Adaptation [5.457279006229213]
  We propose the Invariant Representation Anomaly Detection (IRAD) to solve this problem. The extraction is achieved by an across-domain encoder trained together with source-specific encoders and generators by adversarial learning. We evaluate IRAD extensively on digits images datasets (MNIST, USPS and SVHN) and object recognition datasets (Office-Home)
  arXiv  Detail & Related papers  (2020-06-05T21:05:19Z)

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.