  Compression and reconstruction of visual data have been widely studied in the
computer vision community, even before the popularization of deep learning.
More recently, some have used deep learning to improve or refine existing
pipelines, while others have proposed end-to-end approaches, including
autoencoders and implicit neural representations, such as SIREN and NeRV. In
this work, we propose Neural Visual Representation with Content-adaptive
Embedding (CNeRV), which combines the generalizability of autoencoders with the
simplicity and compactness of implicit representation. We introduce a novel
content-adaptive embedding that is unified, concise, and internally
(within-video) generalizable, that compliments a powerful decoder with a
single-layer encoder. We match the performance of NeRV, a state-of-the-art
implicit neural representation, on the reconstruction task for frames seen
during training while far surpassing for frames that are skipped during
training (unseen images). To achieve similar reconstruction quality on unseen
images, NeRV needs 120x more time to overfit per-frame due to its lack of
internal generalization. With the same latent code length and similar model
size, CNeRV outperforms autoencoders on reconstruction of both seen and unseen
images. We also show promising results for visual data compression. More
details can be found in the project pagehttps://haochen-rye.github.io/CNeRV/
