CauseCollab：面向异构协同感知的因果统一模态无关网络

中文摘要 AI 辅助

协同感知通过多智能体信息共享提升环境理解能力，但其在真实场景中的性能受限于异构传感器模态与模型架构。近期基于协议的两阶段方法通过将异构特征映射到共享协议空间缓解该问题，但独立训练的模态特定转换器常生成模态特定的伪协议分布，导致语义不一致与误差累积，在模态差异大的场景中尤为明显。为解决此问题，本文提出CauseCollab，一种因果统一模态无关网络。CauseCollab从因果角度构建协议空间的表示学习，通过因果度量学习显式将语义因素与模态特定统计混杂因素解耦；同时采用上下文引导的统一转换器处理异构模态，确保跨模态语义一致性。此外，集成新模态仅需训练参数极少的适配器。在OPV2V与DAIR-V2X数据集上的大量实验表明，CauseCollab达到了SOTA性能，在模态差距大的场景中增益更显著。

英文摘要

Collaborative perception enhances environment understanding through multi-agent information sharing, but its performance in real-world scenarios is constrained by heterogeneous sensor modalities and model architectures. Recent protocol-based two-stage methods alleviate this problem by mapping heterogeneous features into a shared protocol space; however, independently trained modality-specific converters often generate modality-specific pseudo-protocol distributions, leading to semantic inconsistency and error accumulation, which is particularly pronounced in scenarios with large modality discrepancies. To address this issue, we propose CauseCollab, a causal unified and modality-agnostic network. CauseCollab formulates representation learning in the protocol space from a causal perspective, explicitly disentangling semantic factors from modality-specific statistical confounders via causal metric learning. Meanwhile, CauseCollab adopts context-guided Unified Converter for heterogeneous modalities to ensure cross-modal semantic consistency. In addition, integrating new modalities only requires training adapters with minimal parameters. Extensive experiments on the OPV2V and DAIR-V2X datasets demonstrate that CauseCollab achieves state-of-the-art performance, with more significant gains in scenarios involving large modality gaps.

发表机构

• State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）
• Beijing University of Posts and Telecommunications（北京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。

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17 pages, accepted at ICML 2026