What Makes Good Collaborative Views? Contrastive Mutual Information
  Maximization for Multi-Agent Perception
Wanfang Su Lixing Chen Yang Bai Xi Lin Gaolei Li Zhe Qu Pan Zhou
Multi-agent perception (MAP) allows autonomous systems to understand complex
environments by interpreting data from multiple sources. This paper
investigates intermediate collaboration for MAP with a specific focus on
exploring "good" properties of collaborative view (i.e., post-collaboration
feature) and its underlying relationship to individual views (i.e.,
pre-collaboration features), which were treated as an opaque procedure by most
existing works. We propose a novel framework named CMiMC (Contrastive Mutual
Information Maximization for Collaborative Perception) for intermediate
collaboration. The core philosophy of CMiMC is to preserve discriminative
information of individual views in the collaborative view by maximizing mutual
information between pre- and post-collaboration features while enhancing the
efficacy of collaborative views by minimizing the loss function of downstream
tasks. In particular, we define multi-view mutual information (MVMI) for
intermediate collaboration that evaluates correlations between collaborative
views and individual views on both global and local scales. We establish
CMiMNet based on multi-view contrastive learning to realize estimation and
maximization of MVMI, which assists the training of a collaboration encoder for
voxel-level feature fusion. We evaluate CMiMC on V2X-Sim 1.0, and it improves
the SOTA average precision by 3.08% and 4.44% at 0.5 and 0.7 IoU
(Intersection-over-Union) thresholds, respectively. In addition, CMiMC can
reduce communication volume to 1/32 while achieving performance comparable to
SOTA. Code and Appendix are released at https://github.com/77SWF/CMiMC.