SegLLM: Multi-round Reasoning Segmentation with Large Language Model

ICLR 2025 Conference Submission1230 Authors

Keywords: LLMs, Reasoning Segmentation, Muiti-round Conversations

Abstract: We present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual outputs. By leveraging a mask-aware multimodal LLM, SegLLM re-integrates previous segmentation results into its input stream, enabling it to reason about complex user intentions and segment objects in relation to previously identified entities, including positional, interactional, and hierarchical relationships, across multiple interactions. This capability allows SegLLM to respond to visual and text queries in a chat-like manner. Evaluated on the newly curated MRSeg benchmark, SegLLM outperforms existing methods in multi-round interactive reasoning segmentation by over 20%. In addition, SegLLM obtains a 5.5% improvement in cIoU for standard single-round referring segmentation and a 4.5% increase in Acc@0.5 for referring expression comprehension.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 1230