SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

2025-05-21

AI Summary

Existing hypergraph neural networks (HGNNs) for visual recognition rely on static, hard hyperedge assignments, leading to hyperedge redundancy, neglect of semantic continuity, and limited capacity to model high-order relationships. To address these limitations, we propose SoftHGNN—a soft hypergraph neural network that introduces learnable hyperedge prototypes and a similarity-driven, continuous membership function, enabling dynamic, differentiable soft vertex-to-hyperedge assignments. Integrated with Top-k sparsification and load-balancing regularization, SoftHGNN balances representational power and computational efficiency. To our knowledge, this is the first work to introduce soft hyperedge modeling into general-purpose visual recognition. Extensive experiments across five benchmark datasets and three vision tasks demonstrate consistent and significant improvements over state-of-the-art methods. SoftHGNN effectively captures scene-level high-order semantic structures, offering both theoretical novelty and practical applicability.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Visual recognition relies on understanding both the semantics of image tokens and the complex interactions among them. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics. To overcome these issues, we present Soft Hypergraph Neural Networks (SoftHGNNs), which extend the methodology of hypergraph computation, to make it truly efficient and versatile in visual recognition tasks. Our framework introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous participation weights rather than hard binary assignments. This dynamic and differentiable association is achieved by using the learnable hyperedge prototype. Through similarity measurements between token features and the prototype, the model generates semantically rich soft hyperedges. SoftHGNN then aggregates messages over soft hyperedges to capture high-order semantics. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements.

Problem

• Modeling high-order interactions in visual recognition tasks
• Reducing redundant computation in self-attention methods
• Overcoming static hyperedge limitations in hypergraph neural networks

Innovation

• Soft hyperedges with continuous participation weights
• Learnable hyperedge prototype for dynamic associations
• Sparse hyperedge selection with top-k activation