A fundamental problem in learning robust and expressive visual
representations lies in efficiently estimating the spatial relationships of
visual semantics throughout the entire image. In this study, we propose vHeat,
a novel vision backbone model that simultaneously achieves both high
computational efficiency and global receptive field. The essential idea,
inspired by the physical principle of heat conduction, is to conceptualize
image patches as heat sources and model the calculation of their correlations
as the diffusion of thermal energy. This mechanism is incorporated into deep
models through the newly proposed module, the Heat Conduction Operator (HCO),
which is physically plausible and can be efficiently implemented using DCT and
IDCT operations with a complexity of $\mathcal{O}(N^{1.5})$. Extensive
experiments demonstrate that vHeat surpasses Vision Transformers (ViTs) across
various vision tasks, while also providing higher inference speeds, reduced
FLOPs, and lower GPU memory usage for high-resolution images. The code will be
released at this https URL

通过模拟热传导原理，本研究提出了 vHeat，一种新颖的视觉模型，可以同时实现高计算效率和全局感受野，并通过 Heat Conduction Operator (HCO) 模块将其应用于深度模型中。广泛的实验证明 vHeat 在各种视觉任务上超越了 Vision Transformers (ViTs)，同时为高分辨率图像提供了更高的推理速度、降低的 FLOPs 和较低的 GPU 内存使用。