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 this https URL

探索中介协作视角下协作感知的 "良好" 特性及其与个体视角的潜在关系，提出了一个名为 CMiMC 的新框架，利用多视角对比学习建立了 CMiMNet，实现了多视图互信息的估计和最大化，提高了性能并减少通信量。

什么造就良好的协作视角？多智能体感知的对比互信息最大化

What Makes Good Collaborative Views? Contrastive Mutual Information  Maximization for Multi-Agent Perception

Existing multi-agent perception systems assume that every agent utilizes the
same model with identical parameters and architecture. The performance can be
degraded with different perception models due to the mismatch in their
confidence scores. In this work, we propose a model-agnostic multi-agent
perception framework to reduce the negative effect caused by the model
discrepancies without sharing the model information. Specifically, we propose a
confidence calibrator that can eliminate the prediction confidence score bias.
Each agent performs such calibration independently on a standard public
database to protect intellectual property. We also propose a corresponding
bounding box aggregation algorithm that considers the confidence scores and the
spatial agreement of neighboring boxes. Our experiments shed light on the
necessity of model calibration across different agents, and the results show
that the proposed framework improves the baseline 3D object detection
performance of heterogeneous agents.

本文提出了一种基于模型无关且不共享模型信息的多智能体感知框架，旨在消除模型差异性对性能的负面影响。具体地，我们提出了一种置信度校准器，可消除预测置信度评分偏差，并为此独立地在标准公共数据库上进行校准。此外，我们还提出了相应的边界框聚合算法，考虑了相邻框的置信度和空间一致性。我们的实验阐明了跨不同代理人的模型校准的必要性，结果表明所提出的框架提高了异构代理人的基线 3D 物体检测性能。

模型无关的多智能体感知框架

Model-Agnostic Multi-Agent Perception Framework

To promote better performance-bandwidth trade-off for multi-agent perception,
we propose a novel distilled collaboration graph (DiscoGraph) to model
trainable, pose-aware, and adaptive collaboration among agents. Our key
novelties lie in two aspects. First, we propose a teacher-student framework to
train DiscoGraph via knowledge distillation. The teacher model employs an early
collaboration with holistic-view inputs; the student model is based on
intermediate collaboration with single-view inputs. Our framework trains
DiscoGraph by constraining post-collaboration feature maps in the student model
to match the correspondences in the teacher model. Second, we propose a
matrix-valued edge weight in DiscoGraph. In such a matrix, each element
reflects the inter-agent attention at a specific spatial region, allowing an
agent to adaptively highlight the informative regions. During inference, we
only need to use the student model named as the distilled collaboration network
(DiscoNet). Attributed to the teacher-student framework, multiple agents with
the shared DiscoNet could collaboratively approach the performance of a
hypothetical teacher model with a holistic view. Our approach is validated on
V2X-Sim 1.0, a large-scale multi-agent perception dataset that we synthesized
using CARLA and SUMO co-simulation. Our quantitative and qualitative
experiments in multi-agent 3D object detection show that DiscoNet could not
only achieve a better performance-bandwidth trade-off than the state-of-the-art
collaborative perception methods, but also bring more straightforward design
rationale. Our code is available on this https URL

该研究提出了一种基于教师 - 学生框架和较大自适应度的 distilled collaboration graph 和网络，使多代理协作感知的性能 - 带宽折衷达到一个更好的平衡，并且实验结果证明其在 3D 物体检测中表现更好。