# A Real-time Global Inference Network for One-stage Referring Expression Comprehension

arXiv: https://arxiv.org/abs/1912.03478 · HTML: https://mlsanity.com/paper/1912.03478/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2019.

## Built on by (most cited first, 30 of 32)

- [SeqTR: A Simple yet Universal Network for Visual Grounding](https://mlsanity.com/paper/2203.16265/) (2022)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [Described Object Detection: Liberating Object Detection with Flexible Expressions](https://mlsanity.com/paper/2307.12813/) (2023)
- [SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion](https://mlsanity.com/paper/2409.17531/) (2024)
- [Language Adaptive Weight Generation for Multi-task Visual Grounding](https://mlsanity.com/paper/2306.04652/) (2023)
- [OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling](https://mlsanity.com/paper/2410.08021/) (2024)
- [Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks](https://mlsanity.com/paper/2204.07780/) (2022)
- [Referring Camouflaged Object Detection](https://mlsanity.com/paper/2306.07532/) (2023)
- [ScanFormer: Referring Expression Comprehension by Iteratively Scanning](https://mlsanity.com/paper/2406.18048/) (2024)
- [Towards Language-guided Visual Recognition via Dynamic Convolutions](https://mlsanity.com/paper/2110.08797/) (2021)
- [Advancing Visual Grounding with Scene Knowledge: Benchmark and Method](https://mlsanity.com/paper/2307.11558/) (2023)
- [Referring Expression Comprehension Using Language Adaptive Inference](https://mlsanity.com/paper/2306.04451/) (2023)
- [Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network](https://mlsanity.com/paper/2301.03160/) (2023)
- [Learning to Evaluate Performance of Multi-modal Semantic Localization](https://mlsanity.com/paper/2209.06515/) (2022)
- [Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension](https://mlsanity.com/paper/2501.01416/) (2025)
- [M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension](https://mlsanity.com/paper/2407.01131/) (2024)
- [Correspondence Matters for Video Referring Expression Comprehension](https://mlsanity.com/paper/2207.10400/) (2022)
- [PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination](https://mlsanity.com/paper/2509.04833/) (2025)
- [WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation](https://mlsanity.com/paper/2505.18686/) (2025)
- [Self-paced Multi-grained Cross-modal Interaction Modeling for Referring Expression Comprehension](https://mlsanity.com/paper/2204.09957/) (2022)
- [NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning](https://mlsanity.com/paper/2310.10975/) (2023)
- [Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization](https://mlsanity.com/paper/2403.06702/) (2024)
- [Improving Generalized Visual Grounding with Instance-aware Joint Learning](https://mlsanity.com/paper/2509.13747/) (2025)
- [ACTRESS: Active Retraining for Semi-supervised Visual Grounding](https://mlsanity.com/paper/2407.03251/) (2024)
- [New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration](https://mlsanity.com/paper/2502.20104/) (2025)
- [A Survivor in the Era of Large-Scale Pretraining: An Empirical Study of One-Stage Referring Expression Comprehension](https://mlsanity.com/paper/2204.07913/) (2022)
- [Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models](https://mlsanity.com/paper/2309.01479/) (2023)
- [Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction for Visual Grounding](https://mlsanity.com/paper/2410.23570/) (2024)
- [Towards Omni-supervised Referring Expression Segmentation](https://mlsanity.com/paper/2311.00397/) (2023)
- [A Simple and Better Baseline for Visual Grounding](https://mlsanity.com/paper/2510.10587/) (2025)

## Abstract (arXiv)

Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description. Most existing REC methods follow a multi-stage pipeline, which are computationally expensive and greatly limit the application of REC. In this paper, we propose a one-stage model towards real-time REC, termed Real-time Global Inference Network (RealGIN). RealGIN addresses the diversity and complexity issues in REC with two innovative designs: the Adaptive Feature Selection (AFS) and the Global Attentive ReAsoNing unit (GARAN). AFS adaptively fuses features at different semantic levels to handle the varying content of expressions. GARAN uses the textual feature as a pivot to collect expression-related visual information from all regions, and thenselectively diffuse such information back to all regions, which provides sufficient context for modeling the complex linguistic conditions in expressions. On five benchmark datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, ReferIt and Flickr30k, the proposed RealGIN outperforms most prior works and achieves very competitive performances against the most advanced method, i.e., MAttNet. Most importantly, under the same hardware, RealGIN can boost the processing speed by about 10 times over the existing methods.