Estimating perceptual attributes of materials directly from images is a
challenging task due to their complex, not fully-understood interactions with
external factors, such as geometry and lighting. Supervised deep learning
models have recently been shown to outperform traditional approaches, but rely
on large datasets of human-annotated images for accurate perception
predictions. Obtaining reliable annotations is a costly endeavor, aggravated by
the limited ability of these models to generalise to different aspects of
appearance. In this work, we show how a much smaller set of human annotations
("strong labels") can be effectively augmented with automatically derived "weak
labels" in the context of learning a low-dimensional image-computable gloss
metric. We evaluate three alternative weak labels for predicting human gloss
perception from limited annotated data. Incorporating weak labels enhances our
gloss prediction beyond the current state of the art. Moreover, it enables a
substantial reduction in human annotation costs without sacrificing accuracy,
whether working with rendered images or real photographs.

从图像中直接估计材料的感知属性是一项具有挑战性的任务，本研究通过使用强标签和弱标签的组合学习了一个低维图像可计算光泽度度量标准，提高了光泽度的预测准确性，并在减少人工标注成本的同时保持了准确性。