Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals
<|reference_start|>Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals: Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations. Typically, INR is parameterized by a multiplayer perceptron (MLP) which takes the coordinates as the inputs and generates corresponding attributes of a signal. However, MLP-based INRs face two critical issues: i) individually considering each coordinate while ignoring the connections; ii) suffering from the spectral bias thus failing to learn high-frequency components. While target visual signals usually exhibit strong local structures and neighborhood dependencies, and high-frequency components are significant in these signals, the issues harm the representational capacity of INRs. This paper proposes Conv-INR, the first INR model fully based on convolution. Due to the inherent attributes of convolution, Conv-INR can simultaneously consider adjacent coordinates and learn high-frequency components effectively. Compared to existing MLP-based INRs, Conv-INR has better representational capacity and trainability without requiring primary function expansion. We conduct extensive experiments on four tasks, including image fitting, CT/MRI reconstruction, and novel view synthesis, Conv-INR all significantly surpasses existing MLP-based INRs, validating the effectiveness. Finally, we raise three reparameterization methods that can further enhance the performance of the vanilla Conv-INR without introducing any extra inference cost.<|reference_end|>
@article{cai2024conv-inr:,
    title={Conv-INR: Convolutional Implicit Neural Representation for Multimodal
  Visual Signals},
    author={Zhicheng Cai},
    journal={arXiv preprint arXiv:2406.04249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04249},
    primaryClass={cs.CV}
}