Personalized Image Semantic Segmentation

• URL: http://arxiv.org/abs/2107.13978v1
• Date: Sat, 24 Jul 2021 04:03:11 GMT
• Title: Personalized Image Semantic Segmentation
• Authors: Yu Zhang and Chang-Bin Zhang and Peng-Tao Jiang and Feng Mao and Ming-Ming Cheng
• Abstract summary: We generate more accurate segmentation results on unlabeled personalized images by investigating the data's personalized traits. We propose a baseline method that incorporates the inter-image context when segmenting certain images. The code and the PIS dataset will be made publicly available.
• Score: 58.980245748434
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Semantic segmentation models trained on public datasets have achieved great success in recent years. However, these models didn't consider the personalization issue of segmentation though it is important in practice. In this paper, we address the problem of personalized image segmentation. The objective is to generate more accurate segmentation results on unlabeled personalized images by investigating the data's personalized traits. To open up future research in this area, we collect a large dataset containing various users' personalized images called PIS (Personalized Image Semantic Segmentation). We also survey some recent researches related to this problem and report their performance on our dataset. Furthermore, by observing the correlation among a user's personalized images, we propose a baseline method that incorporates the inter-image context when segmenting certain images. Extensive experiments show that our method outperforms the existing methods on the proposed dataset. The code and the PIS dataset will be made publicly available.

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