# ScanFormer: Referring Expression Comprehension by Iteratively Scanning

arXiv: https://arxiv.org/abs/2406.18048 · HTML: https://mlsanity.com/paper/2406.18048/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 57 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Token Merging: Your ViT But Faster](https://mlsanity.com/paper/2210.09461/) (2022)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding](https://mlsanity.com/paper/2104.12763/) (2021)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [MAttNet: Modular Attention Network for Referring Expression Comprehension](https://mlsanity.com/paper/1801.08186/) (2018)
- [Large-Scale Adversarial Training for Vision-and-Language Representation Learning](https://mlsanity.com/paper/2006.06195/) (2020)
- [Visual7W: Grounded Question Answering in Images](https://mlsanity.com/paper/1511.03416/) (2015)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Modeling Context Between Objects for Referring Expression Understanding](https://mlsanity.com/paper/1608.00525/) (2016)
- [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)
- [Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition](https://mlsanity.com/paper/2105.15075/) (2021)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [Referring Transformer: A One-step Approach to Multi-task Visual Grounding](https://mlsanity.com/paper/2106.03089/) (2021)

## Built on by (most cited first, 18 of 18)

- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints](https://mlsanity.com/paper/2501.06710/) (2025)
- [PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination](https://mlsanity.com/paper/2509.04833/) (2025)
- [SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding](https://mlsanity.com/paper/2502.16786/) (2025)
- [LVLMs are Bad at Overhearing Human Referential Communication](https://mlsanity.com/paper/2509.11514/) (2025)
- [MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension](https://mlsanity.com/paper/2409.13609/) (2024)
- [GVDIFF: Grounded Text-to-Video Generation with Diffusion Models](https://mlsanity.com/paper/2407.01921/) (2024)
- [CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition](https://mlsanity.com/paper/2407.03632/) (2024)
- [LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel](https://mlsanity.com/paper/2604.20368/) (2026)
- [What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?](https://mlsanity.com/paper/2505.22850/) (2025)
- [Guideline-Consistent Segmentation via Multi-Agent Refinement](https://mlsanity.com/paper/2509.04687/) (2025)
- [Beyond Language: Grounding Referring Expressions with Hand Pointing in Egocentric Vision](https://mlsanity.com/paper/2603.26646/) (2026)
- [A Simple and Better Baseline for Visual Grounding](https://mlsanity.com/paper/2510.10587/) (2025)
- [What "Not" to Detect: Negation-Aware VLMs via Structured Reasoning and Token Merging](https://mlsanity.com/paper/2510.13232/) (2025)
- [Prototype-Aware Multimodal Alignment for Open-Vocabulary Visual Grounding](https://mlsanity.com/paper/2509.06291/) (2025)
- [GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery](https://mlsanity.com/paper/2608.18996/) (2026)
- [InsightSeg: Reusing Correction Insights for Guideline-Consistent Segmentation](https://mlsanity.com/paper/2609.02002/) (2026)
- [DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding](https://mlsanity.com/paper/2609.24409/) (2026)

## Abstract (arXiv)

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.