Learning Multiple Dense Prediction Tasks from Partially Annotated Data

• URL: http://arxiv.org/abs/2111.14893v1
• Date: Mon, 29 Nov 2021 19:03:12 GMT
• Title: Learning Multiple Dense Prediction Tasks from Partially Annotated Data
• Authors: Wei-Hong Li, Xialei Liu, Hakan Bilen
• Abstract summary: We look at jointly learning of multiple dense prediction tasks on partially annotated data, which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
• Score: 41.821234589075445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data, which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.

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