Move What Matters: Parameter-Efficient Domain Adaptation via Optimal Transport Flow for Collaborative Perception

• URL: http://arxiv.org/abs/2602.11565v1
• Date: Thu, 12 Feb 2026 04:36:50 GMT
• Title: Move What Matters: Parameter-Efficient Domain Adaptation via Optimal Transport Flow for Collaborative Perception
• Authors: Zesheng Jia, Jin Wang, Siao Liu, Lingzhi Li, Ziyao Huang, Yunjiang Xu, Jianping Wang,
• Abstract summary: FlowAdapt is a parameter-efficient framework grounded in optimal transport theory.<n>We introduce a Wasserstein Greedy Sampling strategy to selectively filter redundant samples.<n> Progressive Knowledge Transfer module is designed to inject compressed early-stage representations into later stages.
• Score: 8.774658029766988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast domain adaptation remains a fundamental challenge for deploying multi-agent systems across diverse environments in Vehicle-to-Everything (V2X) collaborative perception. Despite the success of Parameter-Efficient Fine-Tuning (PEFT) in natural language processing and conventional vision tasks, directly applying PEFT to multi-agent settings leads to significant performance degradation and training instability. In this work, we conduct a detailed analysis and identify two key factors: (i) inter-frame redundancy in heterogeneous sensory streams, and (ii) erosion of fine-grained semantics in deep-layer representations under PEFT adaptation. To address these issues, we propose FlowAdapt, a parameter-efficient framework grounded in optimal transport theory, which minimizes information transport costs across both data distributions and network hierarchies. Specifically, we introduce a Wasserstein Greedy Sampling strategy to selectively filter redundant samples via a bounded covering radius. Furthermore, Progressive Knowledge Transfer module is designed to progressively inject compressed early-stage representations into later stages through learnable pathways, alleviating semantic degradation in late-stage adaptation. Extensive experiments on three benchmarks demonstrate that FlowAdapt achieves state-of-the-art performance with only 1% of trainable parameters, effectively bridging domain gaps with superior sample efficiency and generalization.

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