Multi-Layer Perceptrons (MLPs) make powerful functional representations for
sampling and reconstruction problems involving low-dimensional signals like
images,shapes and light fields. Recent works have significantly improved their
ability to represent high-frequency content by using periodic activations or
positional encodings. This often came at the expense of generalization: modern
methods are typically optimized for a single signal. We present a new
representation that generalizes to multiple instances and achieves
state-of-the-art fidelity. We use a dual-MLP architecture to encode the
signals. A synthesis network creates a functional mapping from a
low-dimensional input (e.g. pixel-position) to the output domain (e.g. RGB
color). A modulation network maps a latent code corresponding to the target
signal to parameters that modulate the periodic activations of the synthesis
network. We also propose a local-functional representation which enables
generalization. The signal's domain is partitioned into a regular grid,with
each tile represented by a latent code. At test time, the signal is encoded
with high-fidelity by inferring (or directly optimizing) the latent code-book.
Our approach produces generalizable functional representations of images,
videos and shapes, and achieves higher reconstruction quality than prior works
that are optimized for a single signal.

本文提出了一种使用双重 Multi-Layer Perceptrons（MLPs）体系结构的信号表示方法，其中合成网络创建了从低维输入到输出域的功能映射，调制网络将目标信号对应的潜在代码映射到调制周期激活的参数，实现了图像、视频和形状的可推广的功能表示，并实现了比单一信号优化的先前工作更高的重建质量。

用调制周期激活实现可推广的本地功能表示

Modulated Periodic Activations for Generalizable Local Functional  Representations

The interpretation of ego motion and scene change is a fundamental task for
mobile robots. Optical flow information can be employed to estimate motion in
the surroundings. Recently, unsupervised optical flow estimation has become a
research hotspot. However, unsupervised approaches are often easy to be
unreliable on partially occluded or texture-less regions. To deal with this
problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow
estimation approach. In terms of the network architecture, we develop an
adaptive modulation network that employs two novel module types, flow
modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove
outliers in challenging regions. As for the training paradigm, we adopt a
co-teaching strategy, where two networks simultaneously teach each other about
challenging regions to further improve accuracy. Experimental results on the
MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our
CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches,
while still running in real time. Our project page is available at
this https URL

本文提出了一种基于适应性调制网络的自监督光流估计方法 CoT-AMFlow，采用流调制模块和代价体积调制模块去除噪点，同时采用师生共同训练策略，实时性能表现优于其他最先进的自监督方法。