Monocular depth estimation (MDE) is inherently ambiguous, as a given image
may result from many different 3D scenes and vice versa. To resolve this
ambiguity, an MDE system must make assumptions about the most likely 3D scenes
for a given input. These assumptions can be either explicit or implicit. In
this work, we demonstrate the use of natural language as a source of an
explicit prior about the structure of the world. The assumption is made that
human language encodes the likely distribution in depth-space of various
objects. We first show that a language model encodes this implicit bias during
training, and that it can be extracted using a very simple learned approach. We
then show that this prediction can be provided as an explicit source of
assumption to an MDE system, using an off-the-shelf instance segmentation model
that provides the labels used as the input to the language model. We
demonstrate the performance of our method on the NYUD2 dataset, showing
improvement compared to the baseline and to random controls.

使用自然语言作为一种显示世界结构的显式先验的研究中，首先证明了语言模型在训练过程中编码了这种隐式偏差，并通过一个简单的学习方法进行提取。然后，通过一个供给 MDE 系统使用的现成实例分割模型提供标签作为语言模型输入的显式假设源。在 NYUD2 数据集上展示了我们方法的性能，并与基准线和随机对照进行了比较的改进。

基于语言的单目深度估计的深度提示

Language-Based Depth Hints for Monocular Depth Estimation

Although reinforcement learning has found widespread use in dense reward
settings, training autonomous agents with sparse rewards remains challenging.
To address this difficulty, prior work has shown promising results when using
not only task-specific demonstrations but also task-agnostic albeit somewhat
related demonstrations. In most cases, the available demonstrations are
distilled into an implicit prior, commonly represented via a single deep net.
Explicit priors in the form of a database that can be queried have also been
shown to lead to encouraging results. To better benefit from available
demonstrations, we develop a method to Combine Explicit and Implicit Priors
(CEIP). CEIP exploits multiple implicit priors in the form of normalizing flows
in parallel to form a single complex prior. Moreover, CEIP uses an effective
explicit retrieval and push-forward mechanism to condition the implicit priors.
In three challenging environments, we find the proposed CEIP method to improve
upon sophisticated state-of-the-art techniques.

本文提出一种名为 CEIP 的方法，通过使用多个并行的流来形成一个单一复杂的先验分布，并使用有效的显式检索和推进机制来改善强化学习中通过利用内在和外在先验信息来解决稀疏奖励的问题。