SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

ArXi:2505.15325v3 Announce Type: replace Visual recognition relies on understanding the semantics of image tokens and their complex interactions. 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.