The goal of self-supervised visual representation learning is to learn
strong, transferable image representations, with the majority of research
focusing on object or scene level. On the other hand, representation learning
at part level has received significantly less attention. In this paper, we
propose an unsupervised approach to object part discovery and segmentation and
make three contributions. First, we construct a proxy task through a set of
objectives that encourages the model to learn a meaningful decomposition of the
image into its parts. Secondly, prior work argues for reconstructing or
clustering pre-computed features as a proxy to parts; we show empirically that
this alone is unlikely to find meaningful parts; mainly because of their low
resolution and the tendency of classification networks to spatially smear out
information. We suggest that image reconstruction at the level of pixels can
alleviate this problem, acting as a complementary cue. Lastly, we show that the
standard evaluation based on keypoint regression does not correlate well with
segmentation quality and thus introduce different metrics, NMI and ARI, that
better characterize the decomposition of objects into parts. Our method yields
semantic parts which are consistent across fine-grained but visually distinct
categories, outperforming the state of the art on three benchmark datasets.
Code is available at the project page:
this https URL

本研究提出了一种非监督学习方法来发现和分割物体部分，通过像素级别的图像重建配合先前任务的代理目标，得到了一种更有意义的部分分解方法，优于现有的三个基准数据集，提供了一种一致的、可视化的物体语义部分表示。