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.

翻译：神经隐式表征（NIR）因其表示复杂高维数据的能力而受到广泛关注。与需要存储和操作单个数据点的显式表征不同，隐式表征通过学习到的映射函数捕获信息，无需显式表示数据点本身。现有方法通常在训练后对神经网络进行剪枝或量化以加快编解码速度，但我们发现传统方法无法将学习到的表征迁移至新视频。本文研究当视频按时间顺序到达时隐式视频表征的连续扩展问题，此时模型仅能访问当前会话中的视频。我们提出一种新型神经视频表征——渐进式神经表征（PNR），该表征基于彩票假说从超网络中找到适用于给定视频的自适应子结构。在每个训练会话中，PNR在保持先前子网络权重不变的前提下，将先前获得的子网络知识迁移至当前视频的表征学习过程，从而几乎完美地保留NIR对先前视频的解码能力（即避免灾难性遗忘）。我们在新型UVG8/17视频序列基准上验证了所提PNR在神经序列视频表征编译中的有效性。