Complementarity-Enhanced and Redundancy-Minimized Collaboration Network for Multi-agent Perception

Abstract

Multi-agent collaborative perception depends on sharing sensory information to improve perception accuracy and robustness, as well as to extend coverage. The cooperative shared information between agents should achieve an equilibrium between redundancy and complementarity, thus creating a concise and composite representation. To this end, this paper presents a complementarity-enhanced and redundancy-minimized collaboration network (CRCNet), for efficiently guiding and supervising the fusion among shared features. Our key novelties lie in two aspects. First, each fused feature is forced to bring about a marginal gain by exploiting a contrastive loss, which can supervise our model to select complementary features. Second, mutual information is applied to measure the dependence between fused feature pairs and the upper bound of mutual information is minimized to encourage independence, thus guiding our model to select irredundant features. Furthermore, the above modules are incorporated into a feature fusion network CRCNet. Our quantitative and qualitative experiments in collaborative object detection show that CRCNet performs better than the state-of-the-art methods.