Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this challenge, our study explores the potential of semi-supervised learning (SSL) for class-agnostic motion prediction. Our SSL framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data by generating pseudo labels through test-time inference. To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module. This module effectively selects reliable pseudo labels and re-generates unreliable ones. Furthermore, we propose two data augmentation strategies: temporal sampling and BEVMix. These strategies facilitate consistency regularization in SSL. Experiments conducted on nuScenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. These results highlight the ability of our method to strike a favorable balance between annotation costs and performance. Code will be available at https://github.com/kwwcv/SSMP.

翻译：类别无关运动预测方法旨在理解开放世界场景中的运动，对自动驾驶系统具有重要价值。然而，以全监督方式训练高性能模型通常需要大量人工标注数据，获取成本高昂且耗时。针对这一挑战，本研究探索了半监督学习在半监督类别无关运动预测中的潜力。我们的半监督学习框架采用基于一致性的自训练范式，通过测试时推理生成伪标签，使模型能够从未标注数据中学习。为提升伪标签质量，我们提出一种新颖的运动选择与再生模块。该模块有效选择可靠伪标签并重新生成不可靠伪标签。此外，我们提出两种数据增强策略：时间采样与BEVMix。这些策略有助于在半监督学习中实施一致性正则化。在nuScenes数据集上的实验表明，仅使用极少量标注数据，我们的半监督学习方法即可大幅超越自监督方法。同时，该方法展现出与弱监督及部分全监督方法相当的性能。这些结果凸显了本文方法在标注成本与性能之间实现良好平衡的能力。代码将发布于https://github.com/kwwcv/SSMP。