Rethinking Implicit Neural Representations for Vision Learners. (arXiv:2211.12040v2 [cs.CV] UPDATED)

Implicit Neural Representations (INRs) are powerful to parameterize
continuous signals in computer vision. However, almost all INRs methods are
limited to low-level tasks, e.g., image/video compression, super-resolution,
and image generation. The questions on how to explore INRs to high-level tasks
and deep networks are still under-explored. Existing INRs methods suffer from
two problems: 1) narrow theoretical definitions of INRs are inapplicable to
high-level tasks; 2) lack of representation capabilities to deep networks.
Motivated by the above facts, we reformulate the …

arxiv vision