Vision Hopfield Memory Networks for Image Recognition
Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition. Despite their empirical success, these architectures remain far from the computational principles of the human brain, often demanding enormous amounts of training data while offering limited interpretability. We propose the Vision Hopfield Memory Network (V-HMN), a brain-inspired vision backbone that integrates hierarchical memory mechanisms across layers with iterative refinement updates. Specifically, V-HMN incorporates local Hopfield modules that provide associative memory dynamics at the image patch level, global Hopfield modules that functio...
| Authors | Jianfeng Wang, Amine M'Charrak, Luk Koska et al. |
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| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 6 June 2026 |
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