Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations, which takes coordinates as inputs and generates corresponding signal values. Since these coordinates contain no semantic features, INR fails to take any semantic information into consideration. However, semantic information has been proven critical in many vision tasks, especially for visual signal representation. This paper proposes a reparameterization method termed as SPW, which encodes the semantic priors to the weights of INR, thus making INR contain semantic information implicitly and enhancing its representational capacity. Specifically, SPW uses the Semantic Neural Network (SNN) to extract both low- and high-level semantic information of the target visual signal and generates the semantic vector, which is input into the Weight Generation Network (WGN) to generate the weights of INR model. Finally, INR uses the generated weights with semantic priors to map the coordinates to the signal values. After training, we only retain the generated weights while abandoning both SNN and WGN, thus SPW introduces no extra costs in inference. Experimental results show that SPW can improve the performance of various INR models significantly on various tasks, including image fitting, CT reconstruction, MRI reconstruction, and novel view synthesis. Further experiments illustrate that model with SPW has lower weight redundancy and learns more novel representations, validating the effectiveness of SPW.

翻译：隐式神经表示（INR）近年来已成为一种有前景的信号表示范式，它以坐标作为输入并生成相应的信号值。由于这些坐标不包含语义特征，INR无法考虑任何语义信息。然而，语义信息已被证明在许多视觉任务中至关重要，特别是对于视觉信号表示。本文提出一种称为SPW的重参数化方法，将语义先验编码至INR的权重中，从而使INR隐式地包含语义信息并增强其表示能力。具体而言，SPW使用语义神经网络（SNN）提取目标视觉信号的低层与高层语义信息并生成语义向量，该向量被输入至权重生成网络（WGN）以生成INR模型的权重。最终，INR使用带有语义先验的生成权重将坐标映射为信号值。训练完成后，我们仅保留生成的权重而舍弃SNN和WGN，因此SPW在推理过程中不会引入额外开销。实验结果表明，SPW能显著提升多种INR模型在图像拟合、CT重建、MRI重建和新视角合成等任务上的性能。进一步实验表明，采用SPW的模型具有更低的权重冗余度并能学习到更具新颖性的表示，验证了SPW的有效性。