# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 4 Conclusions  
This study introduced a simple yet effective framework for category image retrieval. We exploited the Visual Transformer architecture to take advantage of the self-attention mechanism to enhance the robustness of representation vectors. Additionally, we solved the low-resolution image retrieval problem by using contrastive learning with a proper augmentation strategy. The solution proposed here addresses only the case of low-resolution samples; however, the same framework can be applied to other degraded image retrieval systems if a suitable augmentation method is defined. We guarantee the effectiveness of our approach through extensive experiments, both quantitative and qualitative, on several public datasets. However, the limitation of this study is that the retrieval system was only analyzed at the category level. The lack of evaluations at the instance level makes the model a bit inferior to the general-purpose retrieval system, and we wish to tackle this challenge in future studies.  
Conceptualization, J.-Y.K. and G.-H.Y.; methodology, T.-V.D.; software, G.-H.Y.; validation, J.-Y.K. and G.-H.Y.; formal analysis, G.-H.Y.; investigation, T.-V.D.; writing--original draft preparation, T.-V.D.; writing--review and editing, J.-Y.K.; visualization, T.-V.D.; supervision, J.-Y.K.; project administration, J.-Y.K.; funding acquisition, G.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the BK21 FOUR Program (Fostering Outstanding Universities for Research, 5,199,991,714,138) funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF).  
Not applicable.  
**Data Availability Statement:** Datasets involved in this study are public data and available online. The CUB200-2011 can be found at [[http://www.vision.caltech.edu/visipedia/CUB-200.html](http://www.vision.caltech.edu/visipedia/CUB-200.html)]([http://www.vision.caltech.edu/visipedia/CUB-200.html](http://www.vision.caltech.edu/visipedia/CUB-200.html)) (accessed on 20 May 2021). The CARS196 dataset can be found at [[https://ai.stanford.edu/~jkrause/cars/car_dataset.html](https://ai.stanford.edu/~jkrause/cars/car_dataset.html)]([https://ai.stanford.edu/~jkrause/cars/car_dataset.html](https://ai.stanford.edu/~jkrause/cars/car_dataset.html)) (accessed on 20 May 2021). The Stanford Online Products dataset can be found at [[https://cvgl.stanford.edu/projects/lifted_struct/](https://cvgl.stanford.edu/projects/lifted_struct/)]([https://cvgl.stanford.edu/projects/lifted_struct/](https://cvgl.stanford.edu/projects/lifted_struct/)) (accessed on 20 May 2021).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish.  
Figure 7: Top 5 retrieval results of samples from the CUB200-2011 dataset. The first image on each row is the query image. The figure shows comparisons between using attention embeddings (the second row) and without attention (the first row).