Image as Set of Points (2303.01494v1)

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.

• The paper presents Context Clusters that represent an image as an unordered set of points, replacing convolution with a simplified clustering algorithm.
• It achieves competitive results with a top-1 accuracy of 81.0% on ImageNet and strong performance on MS COCO and ADE20K benchmarks.
• By visualizing clustering maps, the method enhances interpretability and demonstrates robust adaptability to point cloud analysis.

Context Clusters: A Novel Paradigm for Visual Representation

The paper "Image as Set of Points" introduces a novel approach to visual representation using a new technique known as Context Clusters (CoCs). This method provides a significant departure from traditional Convolutional Neural Networks (ConvNets) and Vision Transformers (ViTs) by interpreting an image as a set of unorganized points. The objective is to extract features through a simplified clustering algorithm rather than relying on convolutional operations or attention mechanisms.

Overview of Context Clusters Methodology

Context Clusters conceptualize an image as a collection of individual points, each comprising raw features and positional information. The central operation in CoCs is a simplified clustering algorithm that groups these points to extract deep features hierarchically. This process eschews conventional convolution and attention techniques, instead leveraging clustering exclusively for spatial interaction. The approach aims to provide a new perspective on visual representation with potential applications across diverse domains.

The method involves envisioning an image as unordered data points, employing point cloud methodologies to transform it, and utilizing points as the fundamental unit of visual representation. The core component, the context cluster block, executes feature extraction through clustering followed by feature aggregation and dispatching within each cluster.

Numerical Results and Performance Analysis

While not directly targeting state-of-the-art (SOTA) performance, CoCs demonstrate competitive results compared to traditional models. On the ImageNet-1K benchmark, different variants of CoCs achieve performance metrics that are on par with, or even exceed, those of ConvNets and ViTs with specific configurations. For instance, the CoC-Medium variant achieves a top-1 accuracy of 81.0%, surpassing several established models with similar parameter counts.

In downstream tasks, such as object detection on the MS COCO dataset and semantic segmentation on the ADE20K dataset, CoCs show promising transfer capabilities. For instance, the model matches or surpasses benchmarks set by convolution and attention-based architectures when integrated with Mask R-CNN for detection and instance segmentation.

Interpretability and Generalization

A distinguishing feature of CoCs is their gratifying interpretability. The clustering maps provide valuable insights into what the model learns at each layer, an attribute not typically associated with ConvNets or ViTs. By visualizing the clustering process, researchers can gain a clearer understanding of the decisions made by the model.

Moreover, the generalization capacity of CoCs is underscored by their application to point cloud analysis, where they achieve high accuracy on datasets like ScanObjectNN. This adaptability showcases the potential of CoCs to function effectively across various data modalities.

Implications and Future Directions

The introduction of CoCs represents a significant shift in the approach to visual representation. By stepping away from established paradigms and instead using clustering as a core mechanism, this method opens new avenues for interpretability and generalizability in AI models.

Looking forward, future research could explore optimizing the trade-offs between computational efficiency and accuracy in clustering processes. Investigating diverse applications outside the traditional field of image and point cloud data could extend the impact of CoCs. Furthermore, the potential for developing domain-specific variants or hybrids with existing architectures offers a fertile ground for innovation.

In summary, the paper presents a compelling case for rethinking how images can be represented and processed in AI models. Context Clusters might not replace convolution or attention entirely, but they certainly contribute a fresh, versatile perspective that enriches the toolkit of visual representation in artificial intelligence.