Human visual recognition system shows astonishing capability of compressing
visual information into a set of tokens containing rich representations without
label supervision. One critical driving principle behind it is perceptual
grouping. Despite being widely used in computer vision in the early 2010s, it
remains a mystery whether perceptual grouping can be leveraged to derive a
neural visual recognition backbone that generates as powerful representations.
In this paper, we propose the Perceptual Group Tokenizer, a model that entirely
relies on grouping operations to extract visual features and perform
self-supervised representation learning, where a series of grouping operations
are used to iteratively hypothesize the context for pixels or superpixels to
refine feature representations. We show that the proposed model can achieve
competitive performance compared to state-of-the-art vision architectures, and
inherits desirable properties including adaptive computation without
re-training, and interpretability. Specifically, Perceptual Group Tokenizer
achieves 80.3% on ImageNet-1K self-supervised learning benchmark with linear
probe evaluation, marking a new progress under this paradigm.

人类视觉识别系统显示了惊人的能力，能够将视觉信息压缩为一组包含丰富表示的令牌，无需标签监督。本文提出了感知分组标记器，一种完全依赖于分组操作的模型，用于提取视觉特征和进行自监督表示学习，其中一系列分组操作被用于迭代地假设像素或超像素的上下文，以改进特征表示。我们表明，与最先进的视觉架构相比，所提出的模型在性能上具有竞争力，并且具有自适应计算和可解释性等可取的属性。具体而言，感知分组标记器在 ImageNet-1K 自监督学习基准测试中以线性探测评估获得 80.3％的性能，标志着这一范例的新进展。