# Pixelated Semantic Colorization

## TL;DR

This paper introduces a semantic-guided image colorization method that leverages pixelated object semantics to produce more realistic and detailed colorized images from grayscale inputs.

## Contribution

It proposes a novel neural network architecture with dual branches for semantic understanding and color generation, integrating semantic segmentation into the colorization process.

## Key findings

- Outperforms state-of-the-art colorization methods on PASCAL VOC2012 and COCO-stuff datasets.
- Produces more realistic and detailed colorized images.
- Effectively incorporates semantic segmentation labels into the colorization pipeline.

## Abstract

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed convolutional neural network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art.