Large-scale Unsupervised Semantic Segmentation

• URL: http://arxiv.org/abs/2106.03149v1
• Date: Sun, 6 Jun 2021 15:02:11 GMT
• Title: Large-scale Unsupervised Semantic Segmentation
• Authors: Shang-Hua Gao and Zhong-Yu Li and Ming-Hsuan Yang and Ming-Ming Cheng and Junwei Han and Philip Torr
• Abstract summary: We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 40k high-quality semantic segmentation annotations for evaluation.
• Score: 163.3568726730319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Powered by the ImageNet dataset, unsupervised learning on large-scale data has made significant advances for classification tasks. There are two major challenges to allow such an attractive learning modality for segmentation tasks: i) a large-scale benchmark for assessing algorithms is missing; ii) unsupervised shape representation learning is difficult. We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 40k high-quality semantic segmentation annotations for evaluation. Our benchmark has a high data diversity and a clear task objective. We also present a simple yet effective baseline method that works surprisingly well for LUSS. In addition, we benchmark related un/weakly supervised methods accordingly, identifying the challenges and possible directions of LUSS.

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