# Domain-Generalized Adaptive Semantic Communication for Collaborative Perception

arXiv: https://arxiv.org/abs/2608.00056 · HTML: https://mlsanity.com/paper/2608.00056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [SWAD: Domain Generalization by Seeking Flat Minima](https://mlsanity.com/paper/2102.08604/) (2021)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02934/) (2021)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [Invariant Information Bottleneck for Domain Generalization](https://mlsanity.com/paper/2106.06333/) (2021)
- [V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction](https://mlsanity.com/paper/2412.01812/) (2024)
- [V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions](https://mlsanity.com/paper/2403.11371/) (2024)
- [Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck](https://mlsanity.com/paper/2405.09514/) (2024)
- [CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative Perception with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2502.10705/) (2025)
- [Task-Oriented Communication for Vehicle-to-Infrastructure Cooperative Perception](https://mlsanity.com/paper/2407.20748/) (2024)
- [V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2503.15435/) (2025)
- [SComCP: Task-Oriented Semantic Communication for Collaborative Perception](https://mlsanity.com/paper/2507.00895/) (2025)
- [Cross-Modal Semantic Communication for Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2511.20000/) (2025)
- [CoDS: Collaborative Perception via Digital Semantic Communication](https://mlsanity.com/paper/2512.22513/) (2025)

## Abstract (arXiv)

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.