# Position Focused Attention Network for Image-Text Matching

arXiv: https://arxiv.org/abs/1907.09748 · HTML: https://mlsanity.com/paper/1907.09748/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 45)

- [Similarity Reasoning and Filtration for Image-Text Matching](https://mlsanity.com/paper/2101.01368/) (2021)
- [VinVL: Revisiting Visual Representations in Vision-Language Models](https://mlsanity.com/paper/2101.00529/) (2021)
- [HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval](https://mlsanity.com/paper/2103.15049/) (2021)
- [Kaleido-BERT: Vision-Language Pre-training on Fashion Domain](https://mlsanity.com/paper/2103.16110/) (2021)
- [Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval](https://mlsanity.com/paper/2103.11920/) (2021)
- [Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval](https://mlsanity.com/paper/2108.02417/) (2021)
- [CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval](https://mlsanity.com/paper/2405.19149/) (2024)
- [VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words](https://mlsanity.com/paper/2101.00265/) (2021)
- [Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words](https://mlsanity.com/paper/2107.01872/) (2021)
- [Masked Vision-Language Transformer in Fashion](https://mlsanity.com/paper/2210.15110/) (2022)
- [USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval](https://mlsanity.com/paper/2301.06844/) (2023)
- [Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search](https://mlsanity.com/paper/2411.17776/) (2024)
- [Plug-and-Play Regulators for Image-Text Matching](https://mlsanity.com/paper/2303.13371/) (2023)
- [Rethinking Benchmarks for Cross-modal Image-text Retrieval](https://mlsanity.com/paper/2304.10824/) (2023)
- [Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query](https://mlsanity.com/paper/2103.01654/) (2021)
- [Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained Alignment](https://mlsanity.com/paper/2308.14009/) (2023)
- [Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment](https://mlsanity.com/paper/2503.06974/) (2025)
- [FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning](https://mlsanity.com/paper/2210.15028/) (2022)
- [A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval](https://mlsanity.com/paper/2106.02400/) (2021)
- [Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision](https://mlsanity.com/paper/2210.13591/) (2022)
- [Referring Expression Instance Retrieval and A Strong End-to-End Baseline](https://mlsanity.com/paper/2506.18246/) (2025)
- [More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching](https://mlsanity.com/paper/2105.09597/) (2021)
- [Dual Relation Alignment for Composed Image Retrieval](https://mlsanity.com/paper/2309.02169/) (2023)
- [Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval](https://mlsanity.com/paper/2109.05523/) (2021)
- [EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classification](https://mlsanity.com/paper/2305.06923/) (2023)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching](https://mlsanity.com/paper/2404.18114/) (2024)
- [GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning](https://mlsanity.com/paper/2410.15266/) (2024)
- [Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations](https://mlsanity.com/paper/2509.12653/) (2025)
- [Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval](https://mlsanity.com/paper/2105.07391/) (2021)

## Abstract (arXiv)

Image-text matching tasks have recently attracted a lot of attention in the computer vision field. The key point of this cross-domain problem is how to accurately measure the similarity between the visual and the textual contents, which demands a fine understanding of both modalities. In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views. In this work, we integrate the object position clue to enhance the visual-text joint-embedding learning. We first split the images into blocks, by which we infer the relative position of region in the image. Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical large-scale news dataset (Tencent-News) to validate the practical application value of proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method achieves the state-of-art performance on all of these three datasets.