# Segment Augmentation and Differentiable Ranking for Logo Retrieval

arXiv: https://arxiv.org/abs/2209.02482 · HTML: https://mlsanity.com/paper/2209.02482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval](https://mlsanity.com/paper/2007.12163/) (2020)
- [Aggregating Deep Convolutional Features for Image Retrieval](https://mlsanity.com/paper/1510.07493/) (2015)
- [Optimizing Rank-based Metrics with Blackbox Differentiation](https://mlsanity.com/paper/1912.03500/) (2019)
- [Between-class Learning for Image Classification](https://mlsanity.com/paper/1711.10284/) (2017)
- [Cross-dimensional Weighting for Aggregated Deep Convolutional Features](https://mlsanity.com/paper/1512.04065/) (2015)
- [Class-Weighted Convolutional Features for Visual Instance Search](https://mlsanity.com/paper/1707.02581/) (2017)
- [Component-based Attention for Large-scale Trademark Retrieval](https://mlsanity.com/paper/1811.02746/) (2018)
- [A Large-scale Dataset and Benchmark for Similar Trademark Retrieval](https://mlsanity.com/paper/1701.05766/) (2017)
- [Learning Test-time Augmentation for Content-based Image Retrieval](https://mlsanity.com/paper/2002.01642/) (2020)
- [Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval](https://mlsanity.com/paper/2104.07240/) (2021)

## Abstract (arXiv)

Logo retrieval is a challenging problem since the definition of similarity is more subjective compared to image retrieval tasks and the set of known similarities is very scarce. To tackle this challenge, in this paper, we propose a simple but effective segment-based augmentation strategy to introduce artificially similar logos for training deep networks for logo retrieval. In this novel augmentation strategy, we first find segments in a logo and apply transformations such as rotation, scaling, and color change, on the segments, unlike the conventional image-level augmentation strategies. Moreover, we evaluate whether the recently introduced ranking-based loss function, Smooth-AP, is a better approach for learning similarity for logo retrieval. On the large scale METU Trademark Dataset, we show that (i) our segment-based augmentation strategy improves retrieval performance compared to the baseline model or image-level augmentation strategies, and (ii) Smooth-AP indeed performs better than conventional losses for logo retrieval.