# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Qualitative Analysis  
From the quantitative results presented in the previous section, we verify that our best model for a retrieval system is the model with 25 decisive patch embeddings trained with supervised contrastive loss, classification loss, and triplet loss. This section illustrates some qualitative analyses obtained from our method. First of all, we describe that our framework works well across fine-grain datasets, typically CUB200-2011, CARS196, and SOP. Figure 5 shows the retrieval results when the queries are images with mild resolution reduction. It is important to highlight the fact that a successful retrieval system should show that retrieval results match the query in different views, such as varied lighting conditions or points of view. This property partially manifests in our results, for example, a green car in a different viewpoint or the same type of car but in two different colors, red and blue, as illustrated in Figure 5.  
To clarify the purpose of our study, we consider the case where strong resolution reduction is used, as depicted in Figure 6. The result from Figure 6 is concrete proof that our framework can deal with low-resolution images. We speculate that the result might be due to contrastive learning to fine-tune the model with both multiresolution and fine resolution samples. The study may raise concerns about superior performance, which can be addressed by using super-resolution preprocessing. However, training additional models for super-resolution is impractical because of the demand for high computational resources. In addition, in our findings, we believe that the effectiveness of descriptors for feature matching is a result of careful fine-tuning of the model with proper augmentation.  
Finally, we once again address the effectiveness of the attention mechanism, as demonstrated in Figure 7. The attention mechanism grants the ability to focus on the region of interest, that is, the region that contains objects, as displayed in Figure 6. Attention is particularly important when investigating visual objects in a distracting background. The experimental results herein aim to verify that with the help of attention, the retrieval results can be more accurate in fine-grain category-image retrieval and even in sophisticated cases where semantic vision may not be distinguished.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{5}{c}{**Recall@k**} & \multirow{2}{*}{**mAP**} \\ \cline{2-2} \cline{4-7}  & **1** & **2** & **3** & **4** & **5** & \\ \hline ViT-class & 0.94140625 & 0.96875 & 0.976563 & 0.976563 & 0.976563 & 0.937995 \\ ViT-1 & 0.8984375 & 0.949219 & 0.960938 & 0.972656 & 0.976563 & 0.897331 \\ ViT-128 & 0.92578125 & 0.96875 & 0.980469 & 0.992188 & 0.992188 & 0.918958 \\ ViT-25 & 0.94921875 & 0.988281 & 0.992188 & 0.992188 & 0.996094 & 0.947578 \\ CGD & 0.875 & 0.923611 & 0.947917 & 0.958333 & 0.96875 & 0.857604 \\ No fine-tuning & 0.55078125 & 0.648438 & 0.6875 & 0.726563 & 0.757813 & 0.540013 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recall@k and mAP comparison of different representation output via ViT-based encoder.  
Figure 5: Top 5 retrieval results on mild resolution reduction samples. The first image in each row is the query image. The first two rows show samples from the SOP dataset, the next two rows show samples from the CARS196 dataset, and the last two rows show samples from the CUB200-2011 dataset.  
Figure 6: Top 5 retrieval results on aggressive resolution reduction samples from the CUB200-2011 dataset. The first image in each row is the query image.