# Component-based Attention for Large-scale Trademark Retrieval

arXiv: https://arxiv.org/abs/1811.02746 · HTML: https://mlsanity.com/paper/1811.02746/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2102.04016/) (2021)
- [ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining](https://mlsanity.com/paper/2306.12106/) (2023)
- [PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2502.07215/) (2025)
- [Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features](https://mlsanity.com/paper/2205.05419/) (2022)
- [Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval](https://mlsanity.com/paper/2104.07240/) (2021)
- [TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce](https://mlsanity.com/paper/2312.05103/) (2023)
- [Segment Augmentation and Differentiable Ranking for Logo Retrieval](https://mlsanity.com/paper/2209.02482/) (2022)

## Abstract (arXiv)

The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.