# Image-text Retrieval: A Survey on Recent Research and Development

arXiv: https://arxiv.org/abs/2203.14713 · HTML: https://mlsanity.com/paper/2203.14713/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 62 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers](https://mlsanity.com/paper/2004.00849/) (2020)
- [An Empirical Study of Training End-to-End Vision-and-Language Transformers](https://mlsanity.com/paper/2111.02387/) (2021)
- [UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2012.15409/) (2020)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)
- [Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning](https://mlsanity.com/paper/2104.03135/) (2021)
- [Visual Semantic Reasoning for Image-Text Matching](https://mlsanity.com/paper/1909.02701/) (2019)
- [Probabilistic Embeddings for Cross-Modal Retrieval](https://mlsanity.com/paper/2101.05068/) (2021)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data](https://mlsanity.com/paper/2001.07966/) (2020)
- [Similarity Reasoning and Filtration for Image-Text Matching](https://mlsanity.com/paper/2101.01368/) (2021)
- [Learning Two-Branch Neural Networks for Image-Text Matching Tasks](https://mlsanity.com/paper/1704.03470/) (2017)
- [IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval](https://mlsanity.com/paper/2003.03772/) (2020)
- [CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval](https://mlsanity.com/paper/1909.05506/) (2019)
- [Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers](https://mlsanity.com/paper/2103.16553/) (2021)
- [E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning](https://mlsanity.com/paper/2106.01804/) (2021)
- [WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training](https://mlsanity.com/paper/2103.06561/) (2021)
- [Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval](https://mlsanity.com/paper/1906.04402/) (2019)
- [Kaleido-BERT: Vision-Language Pre-training on Fashion Domain](https://mlsanity.com/paper/2103.16110/) (2021)
- [Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training](https://mlsanity.com/paper/2106.13488/) (2021)

## Built on by (most cited first, 30 of 55)

- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning](https://mlsanity.com/paper/2311.12075/) (2023)
- [Number it: Temporal Grounding Videos like Flipping Manga](https://mlsanity.com/paper/2411.10332/) (2024)
- [RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search](https://mlsanity.com/paper/2305.13653/) (2023)
- [Data Poisoning Attacks Against Multimodal Encoders](https://mlsanity.com/paper/2209.15266/) (2022)
- [OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization](https://mlsanity.com/paper/2312.04403/) (2023)
- [Accelerating Transformers with Spectrum-Preserving Token Merging](https://mlsanity.com/paper/2405.16148/) (2024)
- [Mitigating Test-Time Bias for Fair Image Retrieval](https://mlsanity.com/paper/2305.19329/) (2023)
- [Text-based Person Search without Parallel Image-Text Data](https://mlsanity.com/paper/2305.12964/) (2023)
- [State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding](https://mlsanity.com/paper/2309.12482/) (2023)
- [Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning](https://mlsanity.com/paper/2308.12636/) (2023)
- [Image Quality Assessment: From Human to Machine Preference](https://mlsanity.com/paper/2503.10078/) (2025)
- [Mimic In-Context Learning for Multimodal Tasks](https://mlsanity.com/paper/2504.08851/) (2025)
- [BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving](https://mlsanity.com/paper/2401.01065/) (2024)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [The Phantom Menace: Unmasking Privacy Leakages in Vision-Language Models](https://mlsanity.com/paper/2408.01228/) (2024)
- [CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization](https://mlsanity.com/paper/2510.17847/) (2025)
- [Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles](https://mlsanity.com/paper/2211.16504/) (2022)
- [Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction](https://mlsanity.com/paper/2403.10883/) (2024)
- [Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking](https://mlsanity.com/paper/2510.14824/) (2025)
- [TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings](https://mlsanity.com/paper/2603.04772/) (2026)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [A Survey of Route Recommendations: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2403.00284/) (2024)
- [Benchmark Granularity and Model Robustness for Image-Text Retrieval](https://mlsanity.com/paper/2407.15239/) (2024)
- [IIITD-20K: Dense captioning for Text-Image ReID](https://mlsanity.com/paper/2305.04497/) (2023)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [Semi-supervised Text-based Person Search](https://mlsanity.com/paper/2404.18106/) (2024)
- [Robust Duality Learning for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2505.02549/) (2025)
- [Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere](https://mlsanity.com/paper/2505.11029/) (2025)
- [Object-Aware Query Perturbation for Cross-Modal Image-Text Retrieval](https://mlsanity.com/paper/2407.12346/) (2024)

## Abstract (arXiv)

In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. It is designed for the scenarios where the queries are from one modality and the retrieval galleries from another modality. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives. By dissecting an ITR system into two processes: feature extraction and feature alignment, we summarize the recent advance of the ITR approaches from these two perspectives. On top of this, the efficiency-focused study on the ITR system is introduced as the third perspective. To keep pace with the times, we also provide a pioneering overview of the cross-modal pre-training ITR approaches as the fourth perspective. Finally, we outline the common benchmark datasets and valuation metric for ITR, and conduct the accuracy comparison among the representative ITR approaches. Some critical yet less studied issues are discussed at the end of the paper.