PartImageNet: A Large, High-Quality Dataset of Parts
PartImageNet: A Large, High-Quality Dataset of Parts
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DOI：
10.1007/978-3-031-20074-8_8
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发表时间：
2021-12
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影响因子：
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通讯作者：
Ju He;Shuo Yang;Shaokang Yang;Adam Kortylewski;Xiaoding Yuan;Jieneng Chen;Shuai Liu;Cheng Yang;A. Yuille
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文献类型：
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作者：
Ju He;Shuo Yang;Shaokang Yang;Adam Kortylewski;Xiaoding Yuan;Jieneng Chen;Shuai Liu;Cheng Yang;A. Yuille
用部件来表示物体是很自然的。这有可能提高对象识别和分割算法的性能，但也可以帮助下游任务，如活动识别。然而，由于缺乏具有逐像素零件注释的数据集，阻碍了基于零件模型的研究。这在一定程度上是由于标注对象部件的难度和高成本，因此除了人类之外，很少有人这样做（在基于部件的模型上存在大量文献）。为了帮助解决这个问题，我们提出了PartImageNet，这是一个具有部分分割注释的大型高质量数据集。它由来自ImageNet的158个类和大约24,000张图片组成。PartImageNet是独一无二的，因为它在一组一般的类（包括非刚性的、铰接的对象）上提供了零件级注释，而与现有的零件数据集（不包括人类的数据集）相比，它的大小要大一个数量级。它可以用于许多视觉任务，包括对象分割、语义部分分割、少镜头学习和部分发现。我们进行了全面的实验来研究这些任务，并建立了一套基线。
It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also help for downstream tasks like activity recognition. Research on part-based models, however, is hindered by the lack of datasets with per-pixel part annotations. This is partly due to the difficulty and high cost of annotating object parts so it has rarely been done except for humans (where there exists a big literature on part-based models). To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24, 000 images. PartImageNet is unique because it offers part-level annotations on a general set of classes including non-rigid, articulated objects, while having an order of magnitude larger size compared to existing part datasets (excluding datasets of humans). It can be utilized for many vision tasks including Object Segmentation, Semantic Part Segmentation, Few-shot Learning and Part Discovery. We conduct comprehensive experiments which study these tasks and set up a set of baselines.