# Image as Set of Points Source: [Image as Set of Points]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:02:50.787Z Authors: Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu Published: 2023-03-02T18:56:39.000Z ## Abstract What is an image and how to extract latent features? Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolutional operation in local region; Vision Transformers (ViTs) treat an image as a sequence of patches and extract features via attention mechanism in a global range. In this work, we introduce a straightforward and promising paradigm for visual representation, which is called Context Clusters. Context clusters (CoCs) view an image as a set of unorganized points and extract features via simplified clustering algorithm. In detail, each point includes the raw feature (e.g., color) and positional information (e.g., coordinates), and a simplified clustering algorithm is employed to group and extract deep features hierarchically. Our CoCs are convolution- and attention-free, and only rely on clustering algorithm for spatial interaction. Owing to the simple design, we show CoCs endow gratifying interpretability via the visualization of clustering process. Our CoCs aim at providing a new perspective on image and visual representation, which may enjoy broad applications in different domains and exhibit profound insights. Even though we are not targeting SOTA performance, COCs still achieve comparable or even better results than ConvNets or ViTs on several benchmarks. Codes are available at: https://github.com/ma-xu/Context-Cluster. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet Mini](): Image Classification; ImageNet mini; Top-1 Accuracy: 78.39. - [Image Classification on ImageNet-1K 1.0 (val)](): Image Classification; ImageNet-1K 1.0 (val); Top-1 Accuracy: 81. - [Semantic Segmentation on ADE20K (val)](): Semantic segmentation; ADE20K (val); mIoU: 40.8. - [Object Detection on COCO 2017 (val)](): Object Detection; COCO 2017 (val); AP: not recorded. - [Instance Segmentation on COCO 2017 (val)](): Instance Segmentation; COCO 2017 (val); APm: not recorded. - [Image Classification on CIFAR-100 (val)](): Image Classification; CIFAR-100 (val); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()