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SegLLM: Multi-round Reasoning Segmentation

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arxiv 2410.18923 v2 pith:RLFJOOXG submitted 2024-10-24 cs.CV cs.AI

XuDong Wang , Shaolun Zhang , Shufan Li , Konstantinos Kallidromitis , Kehan Li , Yusuke Kato , Kazuki Kozuka , Trevor Darrell This is my paper · ORCID
classification cs.CVcs.AI
keywords segmentationsegllmmulti-roundreasoningreferringenhancesexpressioninteractive
verification ladder T0 review T1 audit T2 compute T3 formal
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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%. Additionally, we observed that training on multi-round reasoning segmentation data enhances performance on standard single-round referring segmentation and localization tasks, resulting in a 5.5% increase in cIoU for referring expression segmentation and a 4.5% improvement in Acc@0.5 for referring expression localization.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HRSeg: High-Resolution Visual Perception and Enhancement for Reasoning Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    HRSeg combines high-resolution image crops, region attention, and cross-attention mask enhancement, improving reasoning segmentation over LLM-Seg by up to 13.2 gIoU points on LLM-Seg40K.

  2. LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LIRA improves referring segmentation and reduces hallucination in multimodal LLMs by fusing semantic and pixel features and interleaving local image regions with text descriptions.

  3. MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images

    cs.CV 2025-11 conditional novelty 5.0 of 10

    MediRound introduces a multi-round, entity-level medical segmentation task, a 177K-dialogue dataset built from SA-Med2D-20M with GPT-5, and a LLaVA-Med/MedSAM baseline whose inference-time judgment-and-correction modu...

  4. Reasoning Segmentation for Images and Videos: A Survey

    cs.CV 2025-05 conditional novelty 3.0 of 10

    The paper organizes the field of reasoning segmentation into image and video tracks, cataloging 26 methods, 12 metrics, and 29 datasets.

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