# Learning Efficient Representations for Image-Based Patent Retrieval

arXiv: https://arxiv.org/abs/2308.13749 · HTML: https://mlsanity.com/paper/2308.13749/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [A Zero-Shot Framework for Sketch-based Image Retrieval](https://mlsanity.com/paper/1807.11724/) (2018)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2103.13990/) (2021)
- [AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection](https://mlsanity.com/paper/2106.05499/) (2021)
- [Graph Convolution Based Efficient Re-Ranking for Visual Retrieval](https://mlsanity.com/paper/2306.08792/) (2023)
- [Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training](https://mlsanity.com/paper/2306.08789/) (2023)
- [CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval](https://mlsanity.com/paper/2104.09918/) (2021)
- [Generative Model for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/1904.08542/) (2019)
- [Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning](https://mlsanity.com/paper/2004.10780/) (2020)

## Built on by (most cited first, 4 of 4)

- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Large Language Model Informed Patent Image Retrieval](https://mlsanity.com/paper/2404.19360/) (2024)
- [Region-aware Image-based Human Action Retrieval with Transformers](https://mlsanity.com/paper/2407.09924/) (2024)
- [Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval](https://mlsanity.com/paper/2506.13496/) (2025)

## Abstract (arXiv)

Patent retrieval has been attracting tremendous interest from researchers in intellectual property and information retrieval communities in the past decades. However, most existing approaches rely on textual and metadata information of the patent, and content-based image-based patent retrieval is rarely investigated. Based on traits of patent drawing images, we present a simple and lightweight model for this task. Without bells and whistles, this approach significantly outperforms other counterparts on a large-scale benchmark and noticeably improves the state-of-the-art by 33.5% with the mean average precision (mAP) score. Further experiments reveal that this model can be elaborately scaled up to achieve a surprisingly high mAP of 93.5%. Our method ranks first in the ECCV 2022 Patent Diagram Image Retrieval Challenge.