Neural Implicit Representations (NIR) have gained significant attention
recently due to their ability to represent complex and high-dimensional data.
Unlike explicit representations, which require storing and manipulating
individual data points, implicit representations capture information through a
learned mapping function without explicitly representing the data points
themselves. They often prune or quantize neural networks after training to
accelerate encoding/decoding speed, yet we find that conventional methods fail
to transfer learned representations to new videos. This work studies the
continuous expansion of implicit video representations as videos arrive
sequentially over time, where the model can only access the videos from the
current session. We propose a novel neural video representation, Progressive
Neural Representation (PNR), that finds an adaptive substructure from the
supernet for a given video based on Lottery Ticket Hypothesis. At each training
session, our PNR transfers the learned knowledge of the previously obtained
subnetworks to learn the representation of the current video while keeping the
past subnetwork weights intact. Therefore it can almost perfectly preserve the
decoding ability (i.e., catastrophic forgetting) of the NIR on previous videos.
We demonstrate the effectiveness of our proposed PNR on the neural sequential
video representation compilation on the novel UVG8/17 video sequence
benchmarks.

本文研究了基于 NIR 的神经序列视频表示方法，提出了一种基于彩票票据假设的逐渐增强的神经视频表示模型 PNR，并在 UVG8/17 视频序列基准上展示了其有效性。