NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception
Congzhang Shao, Quan Yuan, Guiyang Luo, Yue Hu, Danni Wang, Yilin Liu, Rui Pan, Bo Chen, Jinglin Li

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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Speech and dialogue systems · Social Robot Interaction and HRI
