# Recurrent Multimodal Interaction for Referring Image Segmentation

arXiv: https://arxiv.org/abs/1703.07939 · HTML: https://mlsanity.com/paper/1703.07939/

## Key facts

- Cited by 125 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 30 of 125)

- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [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)
- [Generalized Decoding for Pixel, Image, and Language](https://mlsanity.com/paper/2212.11270/) (2022)
- [Vision-Language Transformer and Query Generation for Referring Segmentation](https://mlsanity.com/paper/2108.05565/) (2021)
- [MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions](https://mlsanity.com/paper/2308.08544/) (2023)
- [GSVA: Generalized Segmentation via Multimodal Large Language Models](https://mlsanity.com/paper/2312.10103/) (2023)
- [PolyFormer: Referring Image Segmentation as Sequential Polygon Generation](https://mlsanity.com/paper/2302.07387/) (2023)
- [ReSTR: Convolution-free Referring Image Segmentation Using Transformers](https://mlsanity.com/paper/2203.16768/) (2022)
- [Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation](https://mlsanity.com/paper/2105.01839/) (2021)
- [VLT: Vision-Language Transformer and Query Generation for Referring Segmentation](https://mlsanity.com/paper/2210.15871/) (2022)
- [Cross-Modal Progressive Comprehension for Referring Segmentation](https://mlsanity.com/paper/2105.07175/) (2021)
- [Locate then Segment: A Strong Pipeline for Referring Image Segmentation](https://mlsanity.com/paper/2103.16284/) (2021)
- [Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding](https://mlsanity.com/paper/2503.06287/) (2025)
- [Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2312.12470/) (2023)
- [Beyond One-to-One: Rethinking the Referring Image Segmentation](https://mlsanity.com/paper/2308.13853/) (2023)
- [EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model](https://mlsanity.com/paper/2406.20076/) (2024)
- [OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.09356/) (2023)
- [Zero-shot Referring Image Segmentation with Global-Local Context Features](https://mlsanity.com/paper/2303.17811/) (2023)
- [Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation](https://mlsanity.com/paper/2307.11545/) (2023)
- [RRSIS: Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2306.08625/) (2023)
- [Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation](https://mlsanity.com/paper/2404.03645/) (2024)
- [Contrastive Grouping with Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2309.01017/) (2023)
- [Instance-Specific Feature Propagation for Referring Segmentation](https://mlsanity.com/paper/2204.12109/) (2022)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [Mask Grounding for Referring Image Segmentation](https://mlsanity.com/paper/2312.12198/) (2023)
- [ReMamber: Referring Image Segmentation with Mamba Twister](https://mlsanity.com/paper/2403.17839/) (2024)
- [Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation](https://mlsanity.com/paper/2305.15302/) (2023)
- [Referring Image Segmentation Using Text Supervision](https://mlsanity.com/paper/2308.14575/) (2023)

## Abstract (arXiv)

In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently and then segment images by combining these two types of representations. We argue that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and we propose convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information. We show that our proposed model outperforms the baseline model on benchmark datasets. In addition, we analyze the intermediate output of the proposed multimodal LSTM approach and empirically explain how this approach enforces a more effective word-to-image interaction.