Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals

arXiv:2406.04249v1 Announce Type: new
Abstract: 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 …

abstract arxiv attributes convolutional cs.cv face however inputs mlp multimodal multiplayer paradigm perceptron representation signal type visual