Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data

• URL: http://arxiv.org/abs/2311.04040v1
• Date: Tue, 7 Nov 2023 14:49:54 GMT
• Title: Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data
• Authors: Ho\`ang-\^An L\^e and Minh-Tan Pham
• Abstract summary: We study the joint learning of object detection and semantic segmentation, the two most popular vision problems. We propose employing knowledge distillation to leverage joint-task optimization.
• Score: 4.9914667450658925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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

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