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.
翻译:在图像网络数据集的推动下,未经监督的大规模数据学习在分类任务方面取得了显著进展。在允许这种具有吸引力的分类任务学习模式方面,有两大挑战:一)缺少评估算法的大规模基准;二)未经监督的形状代表学习很困难。我们提出了一个新问题,即大规模、不受监督的语义分割(LUSS),新创建了一个基准数据集,以跟踪研究进展。根据图像网络数据集,我们提出了具有120万个培训图像和40公里高品质语义分割图的图像网络-S数据集,供评价使用。我们的基准具有很高的数据多样性和明确的任务目标。我们还提出了一个简单而有效的基线方法,为LUSS运作得惊人。此外,我们据此对相关、未经监督的方法进行基准评估,确定LUSS的挑战和可能的方向。