  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.
