  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.
