NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception

TL;DR

NegoCollab introduces a negotiation-based method for aligning heterogeneous agents' perceptions to a common representation, improving collaborative perception performance despite domain gaps.

Contribution

It proposes a novel negotiation approach with specialized loss functions to better align diverse agent perceptions to a shared representation.

Findings

Reduces domain gaps in heterogeneous perception models.

Improves collaborative perception accuracy.

Effective feature alignment across diverse agents.

Abstract

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.