# Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data Source: [Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:02.399Z Authors: Ho\\\`ang-\\^An L\\^e, Minh-Tan Pham Published: 2023-11-07T14:49:54.000Z ## Abstract 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()