Learning to Annotate Part Segmentation with Gradient Matching

• URL: http://arxiv.org/abs/2211.03003v1
• Date: Sun, 6 Nov 2022 01:29:22 GMT
• Title: Learning to Annotate Part Segmentation with Gradient Matching
• Authors: Yu Yang, Xiaotian Cheng, Hakan Bilen, Xiangyang Ji
• Abstract summary: This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN. In particular, we formulate the annotator learning as a learning-to-learn problem. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images.
• Score: 58.100715754135685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited.

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