  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 definitions of INRs from a
novel perspective and propose an innovative Implicit Neural Representation
Network (INRN), which is the first study of INRs to tackle both low-level and
high-level tasks. Specifically, we present three key designs for basic blocks
in INRN along with two different stacking ways and corresponding loss
functions. Extensive experiments with analysis on both low-level tasks (image
fitting) and high-level vision tasks (image classification, object detection,
instance segmentation) demonstrate the effectiveness of the proposed method.
