Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.

翻译：当前协同感知方法通常依赖完全标注的数据集，这在实际情况中可能成本高昂。为降低标注成本，部分研究采用稀疏监督学习技术并为缺失实例生成伪标签。然而，这些方法未能实现协调伪标签质量与数量的最优置信度阈值。为解决该问题，我们提出一种端到端的协同感知双师生框架（CoDTS），该框架采用自适应互补学习机制以同时生成高质量与高数量的伪标签。具体而言，主前景挖掘模块基于静态教师的预测生成高质量伪标签；随后，补充前景挖掘模块通过基于动态教师预测自适应识别缺失实例，确保伪标签在质量与数量间的平衡。此外，框架引入邻域锚点采样模块以增强伪标签的表征能力。为促进自适应互补学习，我们采用分阶段训练策略，以互利方式同步训练学生模型与动态教师模型。大量实验表明，CoDTS能有效确保伪标签在质量与数量上的最优平衡，在稀疏监督协同感知领域建立了新的性能标杆。