Dense depth maps have been used as a key element of visual perception tasks.
There have been tremendous efforts to enhance the depth quality, ranging from
optimization-based to learning-based methods. Despite the remarkable progress
for a long time, their applicability in the real world is limited due to
systematic measurement biases such as density, sensing pattern, and scan range.
It is well-known that the biases make it difficult for these methods to achieve
their generalization. We observe that learning a joint representation for input
modalities (e.g., images and depth), which most recent methods adopt, is
sensitive to the biases. In this work, we disentangle those modalities to
mitigate the biases with prompt engineering. For this, we design a novel depth
prompt module to allow the desirable feature representation according to new
depth distributions from either sensor types or scene configurations. Our depth
prompt can be embedded into foundation models for monocular depth estimation.
Through this embedding process, our method helps the pretrained model to be
free from restraint of depth scan range and to provide absolute scale depth
maps. We demonstrate the effectiveness of our method through extensive
evaluations. Source code is publicly available at
this https URL .

通过解耦输入模态（例如图像和深度），我们设计了一种新颖的深度提示模块来减轻测量偏差，提供绝对尺度深度图，并通过广泛的评估证明了其有效性。

传感器无关深度估计的深度提示

Depth Prompting for Sensor-Agnostic Depth Estimation

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS
dataset, are commonly used in algorithmic fairness papers due to benchmarking
practices of comparing algorithms on datasets used in prior work. In many
cases, this data is used as a benchmark to demonstrate good performance without
accounting for the complexities of criminal justice (CJ) processes. However, we
show that pretrial RAI datasets can contain numerous measurement biases and
errors, and due to disparities in discretion and deployment, algorithmic
fairness applied to RAI datasets is limited in making claims about real-world
outcomes. These reasons make the datasets a poor fit for benchmarking under
assumptions of ground truth and real-world impact. Furthermore, conventional
practices of simply replicating previous data experiments may implicitly
inherit or edify normative positions without explicitly interrogating
value-laden assumptions. Without context of how interdisciplinary fields have
engaged in CJ research and context of how RAIs operate upstream and downstream,
algorithmic fairness practices are misaligned for meaningful contribution in
the context of CJ, and would benefit from transparent engagement with normative
considerations and values related to fairness, justice, and equality. These
factors prompt questions about whether benchmarks for intrinsically
socio-technical systems like the CJ system can exist in a beneficial and
ethical way.

利用风险评估工具数据集（RAI），特别是 ProPublica 的 COMPAS 数据集来比较算法性能是常见的，然而我们发现这些数据集可能会存在大量的测量偏差和错误，而且算法公平性应用于 RAI 数据集在对现实世界结果提出要求时具有局限性。此外，如何进行算法公平性实践是受到了对公平，正义和平等价值观的透明参与的影响，而且这些因素引出了一个问题，即这些在本质上是社会技术系统的基准是否可以以有益和合乎道德的方式存在。