  Understanding human instructions to identify the target objects is vital for
perception systems. In recent years, the advancements of Large Language Models
(LLMs) have introduced new possibilities for image segmentation. In this work,
we delve into reasoning segmentation, a novel task that enables segmentation
system to reason and interpret implicit user intention via large language model
reasoning and then segment the corresponding target. Our work on reasoning
segmentation contributes on both the methodological design and dataset
labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg
effectively connects the current foundational Segmentation Anything Model and
the LLM by mask proposals selection. For the dataset, we propose an automatic
data generation pipeline and construct a new reasoning segmentation dataset
named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive
performance compared with existing methods. Furthermore, our proposed pipeline
can efficiently produce high-quality reasoning segmentation datasets. The
LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark
for training and evaluating various reasoning segmentation approaches. Our
code, models and dataset are at https://github.com/wangjunchi/LLMSeg.
