# A Benchmark for Temporal Color Constancy

arXiv: https://arxiv.org/abs/2003.03763 · HTML: https://mlsanity.com/paper/2003.03763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Cascading Convolutional Temporal Colour Constancy](https://mlsanity.com/paper/2106.07955/) (2021)
- [Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy](https://mlsanity.com/paper/2211.07982/) (2022)

## Abstract (arXiv)

Temporal Color Constancy (CC) is a recently proposed approach that challenges the conventional single-frame color constancy. The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color. In temporal CC, multiple frames from the view finder sequence are used to estimate the color. However, there are no realistic large scale temporal color constancy datasets for method evaluation. In this work, a new temporal CC benchmark is introduced. The benchmark comprises of (1) 600 real-world sequences recorded with a high-resolution mobile phone camera, (2) a fixed train-test split which ensures consistent evaluation, and (3) a baseline method which achieves high accuracy in the new benchmark and the dataset used in previous works. Results for more than 20 well-known color constancy methods including the recent state-of-the-arts are reported in our experiments.