# Adjusting Image Attributes of Localized Regions with Low-level Dialogue

arXiv: https://arxiv.org/abs/2002.04678 · HTML: https://mlsanity.com/paper/2002.04678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding](https://mlsanity.com/paper/2410.16472/) (2024)
- [CAISE: Conversational Agent for Image Search and Editing](https://mlsanity.com/paper/2202.11847/) (2022)
- [Visual Editing with LLM-based Tool Chaining: An Efficient Distillation Approach for Real-Time Applications](https://mlsanity.com/paper/2410.02952/) (2024)

## Abstract (arXiv)

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions that tend to correspond to complex editing steps to accomplish. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level commanding terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for a user to choose from. Though compelled to express in low-level terms, a user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. An analysis shows that users generally adapt to utilizing the proposed low-level language interface. In this study, we identify that object segmentation as the key factor to the user satisfaction. Our work demonstrates the advantages of the low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.