FD-DB: Frequency-Decoupled Dual-Branch Network for Unpaired Synthetic-to-Real Domain Translation

• URL: http://arxiv.org/abs/2602.09476v2
• Date: Wed, 11 Feb 2026 10:17:13 GMT
• Title: FD-DB: Frequency-Decoupled Dual-Branch Network for Unpaired Synthetic-to-Real Domain Translation
• Authors: Chuanhai Zang, Jiabao Hu, XW Song,
• Abstract summary: We propose FD-DB, a frequency-decoupled dual-branch model that separates appearance transfer into low-frequency interpretable editing and high-frequency residual compensation.<n>Experiments on the YCB-V dataset show that FD-DB improves real-domain appearance consistency and significantly boosts downstream semantic segmentation performance.
• Score: 2.0687092208681923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic data provide low-cost, accurately annotated samples for geometry-sensitive vision tasks, but appearance and imaging differences between synthetic and real domains cause severe domain shift and degrade downstream performance. Unpaired synthetic-to-real translation can reduce this gap without paired supervision, yet existing methods often face a trade-off between photorealism and structural stability: unconstrained generation may introduce deformation or spurious textures, while overly rigid constraints limit adaptation to real-domain statistics. We propose FD-DB, a frequency-decoupled dual-branch model that separates appearance transfer into low-frequency interpretable editing and high-frequency residual compensation. The interpretable branch predicts physically meaningful editing parameters (white balance, exposure, contrast, saturation, blur, and grain) to build a stable low-frequency appearance base with strong content preservation. The free branch complements fine details through residual generation, and a gated fusion mechanism combines the two branches under explicit frequency constraints to limit low-frequency drift. We further adopt a two-stage training schedule that first stabilizes the editing branch and then releases the residual branch to improve optimization stability. Experiments on the YCB-V dataset show that FD-DB improves real-domain appearance consistency and significantly boosts downstream semantic segmentation performance while preserving geometric and semantic structures.

Related papers

• Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment [27.352639822596146]
  Cross-worker divergence in losses and gradients can remain invisible under conventional monitoring signals.<n>We propose a model-agnostic diagnostic framework that quantifies worker-level consistency using training signals readily available in standard pipelines.
  arXiv  Detail & Related papers  (2026-02-16T04:42:30Z)
• TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning [8.156452885913108]
  Federated Learning (FL) facilitates collaborative model training while preserving data locality.<n>The exchange of gradients renders the system vulnerable to Gradient Inversion Attacks (GIAs)<n>We propose Targeted Interpretable Perturbation (TIP), a novel defense framework that integrates model interpretability with frequency domain analysis.
  arXiv  Detail & Related papers  (2026-02-12T06:32:49Z)
• Generalizing GNNs with Tokenized Mixture of Experts [75.8310720413187]
  We show that improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor.<n>We propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths.<n>Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.
  arXiv  Detail & Related papers  (2026-02-09T22:48:30Z)
• Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification [5.784164305429653]
  This paper proposes a self-supervised cross-domain transfer framework.<n>It learns transferable spectral-spatial joint representations without source labels.<n> Experimental results demonstrate stable classification performance and strong cross-domain adaptability.
  arXiv  Detail & Related papers  (2026-01-26T02:52:35Z)
• Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method [0.0]
  Substation meters play a critical role in monitoring and ensuring the stable operation of power grids.<n>Their detection of cracks and other physical defects is often hampered by a severe scarcity of annotated samples.<n>We propose a novel framework that integrates Conditional Knowledge Embedding and Hypernetwork-Guided Control into a Stable Diffusion pipeline.
  arXiv  Detail & Related papers  (2026-01-14T07:21:57Z)
• Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding [54.05243949024302]
  Existing robust MLLMs rely on implicit training/adaptation that focuses solely on visual encoder generalization.<n>We propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains.<n>Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity.
  arXiv  Detail & Related papers  (2025-12-19T12:56:17Z)
• Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment [92.57576987521107]
  We propose a novel unifiedtransform framework with dual-domain progressive temporal alignment and quality-conditioned mixture-of-expert (QCMoE)<n>QCMoE allows continuous and consistent rate control with appealing R-D performance.<n> Experimental results show that the proposed method achieves competitive R-D performance compared with the state-of-the-arts.
  arXiv  Detail & Related papers  (2025-12-11T09:14:51Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection [30.77558600436759]
  ARAS is a language-conditioned, auto-regressive anomaly synthesis approach.<n>It injects local, text-specified defects into normal images via token-anchored latent editing.<n>It significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies.
  arXiv  Detail & Related papers  (2025-08-05T15:07:32Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Unpaired Adversarial Learning for Single Image Deraining with Rain-Space Contrastive Constraints [61.40893559933964]
  We develop an effective unpaired SID method which explores mutual properties of the unpaired exemplars by a contrastive learning manner in a GAN framework, named as CDR-GAN. Our method performs favorably against existing unpaired deraining approaches on both synthetic and real-world datasets, even outperforms several fully-supervised or semi-supervised models.
  arXiv  Detail & Related papers  (2021-09-07T10:00:45Z)

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.