标题： NegoCollab：异构协同感知的公共表示协商方法 显示英文标题

标题： NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception

摘要： 协作感知通过代理之间的信息共享扩展感知范围，从而提高任务性能。 不可变的异质性在协作感知中是一个重大挑战，因为参与的代理可能使用不同且固定的感知模型。 这导致代理之间共享的中间特征存在领域差距，从而降低协作性能。 将所有代理的特征对齐到一个公共表示可以以较低的训练成本消除领域差距。 然而，在现有方法中，公共表示被指定为特定代理的表示，使得与该特定代理存在显著领域差异的代理难以实现适当的对齐。 本文提出了NegoCollab，一种基于协商公共表示的异质协作方法。 它在训练过程中引入了一个协商者，从每个模态代理的本地表示中推导出公共表示，有效地减少了与各种本地表示的固有领域差距。 在NegoCollab中，通过一对发送器和接收器实现本地表示空间与公共表示空间之间的特征相互转换。 为了更好地将包含多模态信息的公共表示对齐到本地表示，我们除了分布对齐损失外，还引入了结构对齐损失和实用对齐损失来监督训练。 这使得公共表示中的知识能够充分蒸馏到发送器中。 显示英文摘要

摘要： Collaborative perception improves task performance by expanding the perception range through information sharing among agents. . Immutable heterogeneity poses a significant challenge in collaborative perception, as participating agents may employ different and fixed perception models. This leads to domain gaps in the intermediate features shared among agents, consequently degrading collaborative performance. Aligning the features of all agents to a common representation can eliminate domain gaps with low training cost. However, in existing methods, the common representation is designated as the representation of a specific agent, making it difficult for agents with significant domain discrepancies from this specific agent to achieve proper alignment. This paper proposes NegoCollab, a heterogeneous collaboration method based on the negotiated common representation. It introduces a negotiator during training to derive the common representation from the local representations of each modality's agent, effectively reducing the inherent domain gap with the various local representations. In NegoCollab, the mutual transformation of features between the local representation space and the common representation space is achieved by a pair of sender and receiver. To better align local representations to the common representation containing multimodal information, we introduce structural alignment loss and pragmatic alignment loss in addition to the distribution alignment loss to supervise the training. This enables the knowledge in the common representation to be fully distilled into the sender.