Abstract
Driving scene identification, which assigns multiple non-exclusive class labels to a scene, provides the contextual awareness necessary for enhancing autonomous vehicles' ability to understand, reason about, and interact with the complex driving environment. As a multi-label classification problem, it is better tackled via multitasking learning. However, directly training a multi-label classification model for driving scene identification through multitask learning presents two main challenges: acquiring a balanced, comprehensively annotated multi-label dataset and balancing learning across different tasks. This paper introduces a novel learning system that synergizes knowledge acquisition and accumulation (KAA) with consistency-based active learning (CAL) to address those challenges. KAA acquires and accumulates knowledge about scene identification from various single-label datasets via monotask learning. Subsequently, CAL effectively resolves the knowledge gap caused by the discrepancy between the marginal distributions of individual attributes and their joint distribution. An ablation study on our Driving Scene Identification (DSI) dataset demonstrates a 56.1% performance increase over the baseline model pretrained on ImageNet. Of this, KAA accounts for 31.3% of the gain, and CAL contributes 24.8%. Moreover, KAA-CAL stands out as the best performer when compared to state-of-the-art (SOTA) multi-label models on two public datasets, BDD100K and HSD, achieving this while using 85% less data. The DSI dataset and the implementation code for KAA-CAL are available at this https URL .
Abstract (translated)
驾驶场景识别，即为一个场景分配多个非互斥的类别标签，提供了增强自动驾驶车辆理解、推理和与复杂驾驶环境互动所需的情境意识。作为一个多标签分类问题，它更适合通过多任务学习来解决。然而，直接通过多任务学习训练用于驾驶场景识别的多标签分类模型面临两大挑战：获取平衡且全面标注的多标签数据集以及在不同任务之间保持学习平衡。 本文介绍了一种新型的学习系统，该系统将知识获取与积累（KAA）和基于一致性的主动学习（CAL）相结合，以解决上述挑战。通过单标签数据集进行单调任务学习，KAA 从多种来源获取并积累了关于场景识别的知识。随后，CAL 有效地解决了由个体属性的边际分布与其联合分布之间的差异所导致的知识差距。 在我们构建的驾驶场景识别 (DSI) 数据集上的消融研究表明，在预训练于 ImageNet 的基础模型上实现了 56.1% 的性能提升。其中，KAA 贡献了 31.3%，CAL 则贡献了 24.8% 的性能增长。此外，当与两个公开数据集 BDD100K 和 HSD 上的最先进（SOTA）多标签模型进行比较时，KAA-CAL 在使用比这些 SOTA 模型少 85% 数据的情况下表现最佳。 DSI 数据集和 KAA-CAL 的实现代码可在此链接获取：[https URL]。
URL
https://arxiv.org/abs/2506.17101
