Multi-task partially annotated data where each data point is annotated for only a single task are potentially helpful for data scarcity if a network can leverage the inter-task relationship. In this paper, we study the joint learning of object detection and semantic segmentation, the two most popular vision problems, from multi-task data with partial annotations. Extensive experiments are performed to evaluate each task performance and explore their complementarity when a multi-task network cannot optimize both tasks simultaneously. We propose employing knowledge distillation to leverage joint-task optimization. The experimental results show favorable results for multi-task learning and knowledge distillation over single-task learning and even full supervision scenario. All code and data splits are available at https://github.com/lhoangan/multas

翻译：多任务部分标注数据中，每个数据点仅针对单一任务进行标注，若网络能利用任务间关联性，则此类数据对缓解数据稀缺问题具有潜在价值。本文针对计算机视觉领域两大主流任务——目标检测与语义分割，研究基于部分标注多任务数据的联合学习方法。通过大量实验评估各任务性能，并探究多任务网络无法同时优化所有任务时的任务互补性。我们提出采用知识蒸馏方法实现联合任务优化。实验结果表明，多任务学习与知识蒸馏相比单任务学习甚至全监督场景均取得显著优势。所有代码与数据划分均公开于https://github.com/lhoangan/multas