---
title: "AnchorSeg — paper by Qian et al. (2026) — summary and ideas"
description: "Reasoning segmentation asks a model to produce a pixel mask for a target described only indirectly, for example by its… 47 references."
authors: ["Rui Qian", "Chuanhang Deng", "Qiang Huang", "Jian Xiong", "Mingxuan Li", "Yingbo Zhou", "Wei Zhai", "Jintao Chen", "Dejing Dou"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.938/"
doi: "10.18653/v1/2026.acl-long.938"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qian-2026-anchorseg-language-grounded-query-banks/"
---
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# AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation

**Authors:** [Rui Qian](https://md.research.elamur.ai/free/en/authors/rui-qian/), Chuanhang Deng, Qiang Huang, Jian Xiong, [Mingxuan Li](https://md.research.elamur.ai/free/en/authors/mingxuan-li/), [Yingbo Zhou](https://md.research.elamur.ai/free/en/authors/yingbo-zhou/), Wei Zhai, [Jintao Chen](https://md.research.elamur.ai/free/en/authors/jintao-chen/) and [Dejing Dou](https://md.research.elamur.ai/free/en/authors/dejing-dou/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.938/) · [PDF](https://aclanthology.org/2026.acl-long.938.pdf) · [DOI 10.18653/v1/2026.acl-long.938](https://doi.org/10.18653/v1/2026.acl-long.938) · [OpenAlex](https://openalex.org/W7166836347)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Reasoning segmentation asks a model to produce a pixel mask for a target described only indirectly, for example by its function rather than its name. Earlier systems in the LISA line pack all of this into one \<SEG> token embedding. That single vector has to carry both the meaning of the target and its location. AnchorSeg separates these two roles.

The underlying LMM (LLaVA, fine-tuned with LoRA) generates an ordered "query bank":
- several latent reasoning tokens that hold intermediate semantic states;
- a final \<SEG> anchor token that handles localization.

The anchor is compared against image tokens to form a spatial prior, which is added to SAM's visual features. The contextual queries then modulate the result. SAM's decoder receives the whole bank as its prompt. A bidirectional loss, Token–Mask Cycle Consistency (TMCC), aligns the coarse token-level responses with the full-resolution ground-truth masks.

The method sets a new state of the art on the ReasonSeg test set and is also evaluated on RefCOCO(+/g). This suggests that making the reasoning-versus-grounding structure explicit improves language-driven segmentation, at a modest compute cost.

## Key points
- **Main result:** On the ReasonSeg test set, AnchorSeg reaches 67.7% gIoU and 68.1% cIoU. Code and models are released at github.com/rui-qian/AnchorSeg.
- **Component ablation (cIoU):**
  - adding the anchor-derived spatial prior alone gives 71.6%;
  - adding TMCC on top gives 74.0%;
  - also feeding the contextual queries to the decoder gives 75.9%.
- **TMCC ablation:**
  - token-to-mask loss only: 67.4% gIoU / 73.8% cIoU;
  - mask-to-token loss only: drops to 64.4% gIoU;
  - both directions combined: 68.3% gIoU / 75.9% cIoU.
- **Cost (single A100-40GB):**
  - training takes 2.94 s per iteration and 29.89 GB, versus 1.26 s and 23.68 GB for LISA;
  - inference runs at 4.00 FPS, versus 4.68 FPS for LISA and 9.24 FPS for PixelLM;
  - 306.29M of 7.77B parameters are trainable (3.94%).
- **Stated limitations:** the number K of latent tokens is fixed regardless of query complexity, performance depends on the quality of the LMM, and the approach has not been tested on tasks other than segmentation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 167 works · 20,987 citations

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## Cite

Cite as: Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen and Dejing Dou. 2026. AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{qian2026anchorseg,
  title = {{AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation}},
  author = {Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen and Dejing Dou},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.938},
  url = {https://doi.org/10.18653/v1/2026.acl-long.938}
}
```

- [← previous AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models](https://md.research.elamur.ai/free/en/papers/shen-2026-anchormem-anchored-facts-associative-contexts/)
- [next → Ancient Greek to Modern Greek Machine Translation: A Novel Benchmark and Fine-Tuning Experiments on LLMs and NMT Models](https://md.research.elamur.ai/free/en/papers/mavromatis-2026-ancient-greek-modern-greek-machine/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/22/)

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43. Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2024b. Next-GPT: Any-to-any multimodal llm. In ICML. [find](https://scholar.google.com/scholar?q=Shengqiong+Wu%2C+Hao+Fei%2C+Leigang+Qu%2C+Wei+Ji%2C+and+Tat%2DSeng+Chua%2E+2024b%2E+Next%2DGPT%3A+Any%2Dto%2Dany+multimodal+llm%2E+In+ICML%2E)
44. Tsung-Han Wu, Giscard Biamby, David Chan, Lisa Dunlap, Ritwik Gupta, Xudong Wang, Joseph E Gonzalez, and Trevor Darrell. 2024c. See, say, and segment: Teaching lmms to overcome false premises. In CVPR. [find](https://scholar.google.com/scholar?q=Tsung%2DHan+Wu%2C+Giscard+Biamby%2C+David+Chan%2C+Lisa+Dunlap%2C+Ritwik+Gupta%2C+Xudong+Wang%2C+Joseph+E+Gonzalez%2C+and+Trevor+Darrell%2E+2024c%2E+See%2C+say%2C+and+segment%3A+Teaching+lmms+to+overcome+false+premises%2E+In+CVPR)
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*Updated 1 November 2026*