# Predicting Perceived Gloss: Do Weak Labels Suffice?

arXiv: https://arxiv.org/abs/2403.17672 · HTML: https://mlsanity.com/paper/2403.17672/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection](https://mlsanity.com/paper/2004.04725/) (2020)
- [Weakly Supervised Learning with Side Information for Noisy Labeled Images](https://mlsanity.com/paper/2008.11586/) (2020)
- [SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection](https://mlsanity.com/paper/2006.12884/) (2020)
- [The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation](https://mlsanity.com/paper/2303.15062/) (2023)
- [A Similarity Measure for Material Appearance](https://mlsanity.com/paper/1905.01562/) (2019)
- [Strength from Weakness: Fast Learning Using Weak Supervision](https://mlsanity.com/paper/2002.08483/) (2020)
- [Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting](https://mlsanity.com/paper/1708.02731/) (2017)
- [An intuitive control space for material appearance](https://mlsanity.com/paper/1806.04950/) (2018)
- [In-the-wild Material Appearance Editing using Perceptual Attributes](https://mlsanity.com/paper/2302.03619/) (2023)
- [The joint role of geometry and illumination on material recognition](https://mlsanity.com/paper/2101.02496/) (2021)
- [Interpretable Disentangled Parametrization of Measured BRDF with β-VAE](https://mlsanity.com/paper/2208.03914/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Controllable Appearance Representation for Flexible Transfer and Editing](https://mlsanity.com/paper/2504.15028/) (2025)

## Abstract (arXiv)

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.