@inproceedings{wang-etal-2026-words,
title = "From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation",
author = "Wang, Yizhou and
Chiu, Mang Tik and
Zhang, Lingzhi and
Shen, Xuan and
Amirghodsi, Sohrab and
Fu, Yun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2155/",
pages = "46447--46461",
ISBN = "979-8-89176-390-6",
abstract = "Visual segmentation, the task of segmenting an image into semantically meaningful regions, is a cornerstone in machine learning and has widespread applications in industry. Nevertheless, visual segmentation with instruction has been a challenging task for many years. This largely stems from the cross-modal discrepancy between language and image domains, resulting in difficulty in relating the instruction semantics and the pixel-level predictions. In recent years, the remarkable reasoning capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) have spurred a new wave of research aiming to bridge the disparity between natural language instructions and pixel-level understanding. This survey offers the first comprehensive overview of the rapidly evolving field of LLM-driven visual segmentation. We categorize existing approaches based on their core objectives and methodologies, including reasoning-based segmentation, open-vocabulary segmentation, grounding techniques connecting language to pixels, and extensions to video domains. We review recent seminal works in LLM-based visual segmentation, analyzing their architectural innovations, training strategies, and benchmark performance. Furthermore, we discuss the common datasets, evaluation metrics, and identify key challenges and promising future directions at the intersection of language and visual segmentation. We hope this survey serves as a valuable resource for researchers and practitioners seeking to understand the current landscape and future directions of leveraging LLMs for sophisticated visual segmentation tasks and applications. The resource summary is available at https://github.com/wyzjack/Awesome-LLM-Visual-Segmentation."
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<abstract>Visual segmentation, the task of segmenting an image into semantically meaningful regions, is a cornerstone in machine learning and has widespread applications in industry. Nevertheless, visual segmentation with instruction has been a challenging task for many years. This largely stems from the cross-modal discrepancy between language and image domains, resulting in difficulty in relating the instruction semantics and the pixel-level predictions. In recent years, the remarkable reasoning capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) have spurred a new wave of research aiming to bridge the disparity between natural language instructions and pixel-level understanding. This survey offers the first comprehensive overview of the rapidly evolving field of LLM-driven visual segmentation. We categorize existing approaches based on their core objectives and methodologies, including reasoning-based segmentation, open-vocabulary segmentation, grounding techniques connecting language to pixels, and extensions to video domains. We review recent seminal works in LLM-based visual segmentation, analyzing their architectural innovations, training strategies, and benchmark performance. Furthermore, we discuss the common datasets, evaluation metrics, and identify key challenges and promising future directions at the intersection of language and visual segmentation. We hope this survey serves as a valuable resource for researchers and practitioners seeking to understand the current landscape and future directions of leveraging LLMs for sophisticated visual segmentation tasks and applications. The resource summary is available at https://github.com/wyzjack/Awesome-LLM-Visual-Segmentation.</abstract>
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%0 Conference Proceedings
%T From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation
%A Wang, Yizhou
%A Chiu, Mang Tik
%A Zhang, Lingzhi
%A Shen, Xuan
%A Amirghodsi, Sohrab
%A Fu, Yun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-words
%X Visual segmentation, the task of segmenting an image into semantically meaningful regions, is a cornerstone in machine learning and has widespread applications in industry. Nevertheless, visual segmentation with instruction has been a challenging task for many years. This largely stems from the cross-modal discrepancy between language and image domains, resulting in difficulty in relating the instruction semantics and the pixel-level predictions. In recent years, the remarkable reasoning capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) have spurred a new wave of research aiming to bridge the disparity between natural language instructions and pixel-level understanding. This survey offers the first comprehensive overview of the rapidly evolving field of LLM-driven visual segmentation. We categorize existing approaches based on their core objectives and methodologies, including reasoning-based segmentation, open-vocabulary segmentation, grounding techniques connecting language to pixels, and extensions to video domains. We review recent seminal works in LLM-based visual segmentation, analyzing their architectural innovations, training strategies, and benchmark performance. Furthermore, we discuss the common datasets, evaluation metrics, and identify key challenges and promising future directions at the intersection of language and visual segmentation. We hope this survey serves as a valuable resource for researchers and practitioners seeking to understand the current landscape and future directions of leveraging LLMs for sophisticated visual segmentation tasks and applications. The resource summary is available at https://github.com/wyzjack/Awesome-LLM-Visual-Segmentation.
%U https://aclanthology.org/2026.acl-long.2155/
%P 46447-46461
Markdown (Informal)
[From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation](https://aclanthology.org/2026.acl-long.2155/) (Wang et al., ACL 2026)
ACL