# Improving Video Colorization by Test-Time Tuning

arXiv: https://arxiv.org/abs/2307.11757 · HTML: https://mlsanity.com/paper/2307.11757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Tracking Emerges by Colorizing Videos](https://mlsanity.com/paper/1806.09594/) (2018)
- [Coherent Online Video Style Transfer](https://mlsanity.com/paper/1703.09211/) (2017)
- [Deep Exemplar-based Colorization](https://mlsanity.com/paper/1807.06587/) (2018)
- [Deep Exemplar-based Video Colorization](https://mlsanity.com/paper/1906.09909/) (2019)
- [Deep Animation Video Interpolation in the Wild](https://mlsanity.com/paper/2104.02495/) (2021)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better](https://mlsanity.com/paper/2202.12024/) (2022)
- [EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation](https://mlsanity.com/paper/2110.07797/) (2021)
- [Zoom in to the details of human-centric videos](https://mlsanity.com/paper/2005.13222/) (2020)
- [MANet: Improving Video Denoising with a Multi-Alignment Network](https://mlsanity.com/paper/2202.09704/) (2022)
- [Cross-Camera Human Motion Transfer by Time Series Analysis](https://mlsanity.com/paper/2109.14174/) (2021)
- [Cross-Camera Deep Colorization](https://mlsanity.com/paper/2209.01211/) (2022)

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

- [PeCA: Palette Context Assisted Inference for Test-Time Paint-Bucket Colourisation on Animation Videos](https://mlsanity.com/paper/2608.00903/) (2026)

## Abstract (arXiv)

With the advancements in deep learning, video colorization by propagating color information from a colorized reference frame to a monochrome video sequence has been well explored. However, the existing approaches often suffer from overfitting the training dataset and sequentially lead to suboptimal performance on colorizing testing samples. To address this issue, we propose an effective method, which aims to enhance video colorization through test-time tuning. By exploiting the reference to construct additional training samples during testing, our approach achieves a performance boost of 1~3 dB in PSNR on average compared to the baseline. Code is available at: https://github.com/IndigoPurple/T3