# Bring Adaptive Binding Prototypes to Generalized Referring Expression Segmentation

arXiv: https://arxiv.org/abs/2405.15169 · HTML: https://mlsanity.com/paper/2405.15169/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 73 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 30 of 58)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://mlsanity.com/paper/1801.08186/) (2018)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [CRIS: CLIP-Driven Referring Image Segmentation](https://mlsanity.com/paper/2111.15174/) (2021)
- [LAVT: Language-Aware Vision Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2112.02244/) (2021)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [Segmentation from Natural Language Expressions](https://mlsanity.com/paper/1603.06180/) (2016)
- [GRES: Generalized Referring Expression Segmentation](https://mlsanity.com/paper/2306.00968/) (2023)
- [Modeling Context Between Objects for Referring Expression Understanding](https://mlsanity.com/paper/1608.00525/) (2016)
- [Vision-Language Transformer and Query Generation for Referring Segmentation](https://mlsanity.com/paper/2108.05565/) (2021)
- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions](https://mlsanity.com/paper/2308.08544/) (2023)
- [Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation](https://mlsanity.com/paper/2003.08813/) (2020)
- [A Fast and Accurate One-Stage Approach to Visual Grounding](https://mlsanity.com/paper/1908.06354/) (2019)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Language as Queries for Referring Video Object Segmentation](https://mlsanity.com/paper/2201.00487/) (2022)

## Built on by (most cited first, 8 of 8)

- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination](https://mlsanity.com/paper/2509.04833/) (2025)
- [Latent Expression Generation for Referring Image Segmentation and Grounding](https://mlsanity.com/paper/2508.05123/) (2025)
- [GREx: Generalized Referring Expression Segmentation, Comprehension, and Generation](https://mlsanity.com/paper/2601.05244/) (2026)
- [GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions](https://mlsanity.com/paper/2508.21102/) (2025)
- [Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2607.16327/) (2026)
- [Phrase-Instance Alignment for Generalized Referring Segmentation](https://mlsanity.com/paper/2411.15087/) (2024)
- [VIPA: Visual Informative Part Attention for Referring Image Segmentation](https://mlsanity.com/paper/2602.14788/) (2026)

## Abstract (arXiv)

Referring Expression Segmentation (RES) has attracted rising attention, aiming to identify and segment objects based on natural language expressions. While substantial progress has been made in RES, the emergence of Generalized Referring Expression Segmentation (GRES) introduces new challenges by allowing expressions to describe multiple objects or lack specific object references. Existing RES methods, usually rely on sophisticated encoder-decoder and feature fusion modules, and are difficult to generate class prototypes that match each instance individually when confronted with the complex referent and binary labels of GRES. In this paper, reevaluating the differences between RES and GRES, we propose a novel Model with Adaptive Binding Prototypes (MABP) that adaptively binds queries to object features in the corresponding region. It enables different query vectors to match instances of different categories or different parts of the same instance, significantly expanding the decoder's flexibility, dispersing global pressure across all queries, and easing the demands on the encoder. Experimental results demonstrate that MABP significantly outperforms state-of-the-art methods in all three splits on gRefCOCO dataset. Meanwhile, MABP also surpasses state-of-the-art methods on RefCOCO+ and G-Ref datasets, and achieves very competitive results on RefCOCO. Code is available at https://github.com/buptLwz/MABP