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。

感知分组解析器：通过迭代分组构建感知

Perceptual Group Tokenizer: Building Perception with Iterative Grouping

Bilinear models has been shown to achieve impressive performance on a wide
range of visual tasks, such as semantic segmentation, fine grained recognition
and face recognition. However, bilinear features are high dimensional,
typically on the order of hundreds of thousands to a few million, which makes
them impractical for subsequent analysis. We propose two compact bilinear
representations with the same discriminative power as the full bilinear
representation but with only a few thousand dimensions. Our compact
representations allow back-propagation of classification errors enabling an
end-to-end optimization of the visual recognition system. The compact bilinear
representations are derived through a novel kernelized analysis of bilinear
pooling which provide insights into the discriminative power of bilinear
pooling, and a platform for further research in compact pooling methods.
Experimentation illustrate the utility of the proposed representations for
image classification and few-shot learning across several datasets.

本研究提出两种紧凑的双线性表示方法，与完整的双线性表示具有相同的判别能力，但仅具有几千个维度。这些紧凑的表示通过对双线性池化的核分析导出，为其他紧凑池化方法的研究提供了平台。实验证明，提出的表示方法对于多个数据集的图像分类和少样本学习都具有实用价值。