# A System for Automated Image Editing from Natural Language Commands

arXiv: https://arxiv.org/abs/1812.01083 · HTML: https://mlsanity.com/paper/1812.01083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

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

- [Understanding Generative AI Capabilities in Everyday Image Editing Tasks](https://mlsanity.com/paper/2505.16181/) (2025)

## Abstract (arXiv)

This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework composed of actions and entities to map a user's natural language request to executable commands in an image editing program is described. We resolve previously labeled annotator disagreement through a voting process and complete annotation of the corpus. We experimented with different machine learning models and found that the LSTM, the SVM, and the bidirectional LSTM-CRF joint models are the best to detect image editing actions and associated entities in a given utterance.