Domain-Generalized Adaptive Semantic Communication for Collaborative Perception

• URL: http://arxiv.org/abs/2608.00056v1
• Date: Mon, 27 Jul 2026 07:10:48 GMT
• Title: Domain-Generalized Adaptive Semantic Communication for Collaborative Perception
• Abstract summary: RSTA is a domain-generalized semantic communication framework for source-free V2X collaborative perception.<n>RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency.<n>A theoretical task decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode.
• Score: 6.782749441111213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions. In V2X, received semantic tokens suffer coupled degradation from pre-transmission domain drift and in-transit channel corruption; existing methods address only one source, leaving adaptation misled by tokens that are simultaneously off-domain and physically degraded. RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency, and updates a lightweight in-deployment decoder adapter through reliability-gated entropy minimization that restricts gradients to tokens ranked high in both semantic relevance and channel fidelity. A theoretical task robustness decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode. Trained on AWGN and tested on unseen Rayleigh fading, RSTA achieves +7.2 AP@0.7 over pre-deployment domain generalization on cross-weather tasks and +5.5 on cross-dataset tasks across four V2X benchmarks, updating only 0.21\% of parameters in-deployment with zero inter-agent synchronization overhead.

Related papers

• Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation [15.24999553337715]
  We present HAFR-Net, a progressive refinement framework that adaptively organizes and refines hierarchical representations.<n>Under a matched Swin-B training and single-scale inference protocol, HAFR-Net attains 84.12%, 87.86%, 55.17%, and 67.70% mIoU.
  arXiv  Detail & Related papers  (2026-08-16T09:26:58Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication [56.844498058243744]
  6G semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness.<n>Existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss.<n>This paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication.
  arXiv  Detail & Related papers  (2026-04-28T03:32:50Z)
• Generative Semantic Communication via Alternating Dual-Domain Posterior Sampling [3.860428650013287]
  Generative semantic communication (SemCom) harnesses pretrained priors to improve the perceptual quality of wireless image transmission.<n>Existing generative SemCom receivers rely on maximum a posteriori (MAP) estimation.<n>We propose alternating dual-domain posterior sampling (ADDPS), a diffusion-based SemCom receiver.
  arXiv  Detail & Related papers  (2026-04-18T03:00:02Z)
• A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders [5.780557526613627]
  This paper proposes a Transferable Software Requirement Conflict Detection Framework based on SBERT and SimCSE.<n>The proposed framework achieves a 10.4% improvement in both macro-F1 and weighted-F1 scores in in-domain settings, and an 11.4% increase in macro-F1 in cross-domain scenarios.
  arXiv  Detail & Related papers  (2025-11-28T09:16:35Z)
• Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement [13.553355329509243]
  Cross-domain recommendation aims to alleviate the data sparsity by transferring knowledge across domains.<n>Disentangled representation learning provides an effective solution to model complex user preferences.<n>We propose DGCDR, a GNN-enhanced encoder-decoder framework.
  arXiv  Detail & Related papers  (2025-07-23T01:29:45Z)
• 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)
• From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization [12.785100004522059]
  Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data.<n>DVGL methods require obtaining the new paired data and subsequent retraining for model adaptation.<n>We propose a novel cross-domain invariant knowledge transfer network (CDIKTNet) with limited supervision.
  arXiv  Detail & Related papers  (2025-03-10T16:46:43Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensors [6.451060076703026]
  In this paper, deep learning based on a target-domain-free domain adaptation convolutional neural network (TDACNN) is proposed. The main concept is that CNNs extract not only the domain-specific features of samples but also the domain-invariant features underlying both the source and target domains. Experiments on two datasets drift under different settings demonstrate the superiority of TDACNN compared with several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-14T16:30:17Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)

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.