Progress in lighting estimation is tracked by computing existing image
quality assessment (IQA) metrics on images from standard datasets. While this
may appear to be a reasonable approach, we demonstrate that doing so does not
correlate to human preference when the estimated lighting is used to relight a
virtual scene into a real photograph. To study this, we design a controlled
psychophysical experiment where human observers must choose their preference
amongst rendered scenes lit using a set of lighting estimation algorithms
selected from the recent literature, and use it to analyse how these algorithms
perform according to human perception. Then, we demonstrate that none of the
most popular IQA metrics from the literature, taken individually, correctly
represent human perception. Finally, we show that by learning a combination of
existing IQA metrics, we can more accurately represent human preference. This
provides a new perceptual framework to help evaluate future lighting estimation
algorithms.

通过进行一系列实验，我们发现现有的图像质量评估指标并不能正确地代表人们对光照估计方法的偏好，然而通过综合学习多个指标，我们可以更准确地表示人类的首选项，从而为评估未来的光照估计算法提供了新的感知框架。

朝向光照估计的感知评估框架

Towards a Perceptual Evaluation Framework for Lighting Estimation

Objects make distinctive sounds when they are hit or scratched. These sounds
reveal aspects of an object's material properties, as well as the actions that
produced them. In this paper, we propose the task of predicting what sound an
object makes when struck as a way of studying physical interactions within a
visual scene. We present an algorithm that synthesizes sound from silent videos
of people hitting and scratching objects with a drumstick. This algorithm uses
a recurrent neural network to predict sound features from videos and then
produces a waveform from these features with an example-based synthesis
procedure. We show that the sounds predicted by our model are realistic enough
to fool participants in a "real or fake" psychophysical experiment, and that
they convey significant information about material properties and physical
interactions.

本文提出了一种利用递归神经网络从无声视频中合成与物体相互作用产生的声音的算法，并且在 “真实或假的” 心理物理实验中证实了该模型产生的声音足够逼真，能够传达物质特性和物理相互作用的显著信息。

视觉指示音

Visually Indicated Sounds

Human labeled datasets, along with their corresponding evaluation algorithms,
play an important role in boundary detection. We here present a psychophysical
experiment that addresses the reliability of such benchmarks. To find better
remedies to evaluate the performance of any boundary detection algorithm, we
propose a computational framework to remove inappropriate human labels and
estimate the intrinsic properties of boundaries.

本文介绍了一个心理物理实验，研究人员标记的数据集及其对应的评估算法在边界检测中的重要性，提出了一种计算框架来消除不适当的人类标签并估计边界的内在属性，以找到更好的评估任何边界检测算法性能的方法。