Recurrent State-space models (RSSMs) are highly expressive models for
learning patterns in time series data and system identification. However, these
models assume that the dynamics are fixed and unchanging, which is rarely the
case in real-world scenarios. Many control applications often exhibit tasks
with similar but not identical dynamics which can be modeled as a latent
variable. We introduce the Hidden Parameter Recurrent State Space Models
(HiP-RSSMs), a framework that parametrizes a family of related dynamical
systems with a low-dimensional set of latent factors. We present a simple and
effective way of learning and performing inference over this Gaussian graphical
model that avoids approximations like variational inference. We show that
HiP-RSSMs outperforms RSSMs and competing multi-task models on several
challenging robotic benchmarks both on real-world systems and simulations.

介绍了一种名为 HiP-RSSMs 的框架，将相关动态系统参数化为低维潜在因素，并在真实世界的控制任务和仿真中表现出优于 RSSMs 和其他多任务模型的动态行为。

用于动态变化场景的隐式参数循环状态空间模型

Hidden Parameter Recurrent State Space Models For Changing Dynamics  Scenarios

We propose a new method for video object segmentation (VOS) that addresses
object pattern learning from unlabeled videos, unlike most existing methods
which rely heavily on extensive annotated data. We introduce a unified
unsupervised/weakly supervised learning framework, called MuG, that
comprehensively captures intrinsic properties of VOS at multiple granularities.
Our approach can help advance understanding of visual patterns in VOS and
significantly reduce annotation burden. With a carefully-designed architecture
and strong representation learning ability, our learned model can be applied to
diverse VOS settings, including object-level zero-shot VOS, instance-level
zero-shot VOS, and one-shot VOS. Experiments demonstrate promising performance
in these settings, as well as the potential of MuG in leveraging unlabeled data
to further improve the segmentation accuracy.

提出了一种利用无标注视频进行物体模式学习的视频对象分割新方法，通过引入统一的无监督 / 弱监督学习框架，可以全面捕捉 VOS 的内在特性，在不同的 VOS 设置中表现出有希望的性能并显著减少标注负担。