Recent empirical works reveal that visual representation learned by deep neural networks can be successfully used as descriptors for image retrieval. A common technique is to leverage pre-trained models to learn visual descriptors by ranking losses and fine-tuning with labeled data. However, retrieval systems’ performance significantly decreases when querying images of lower resolution than the training images. This study considered a contrastive learning framework fine-tuned on features extracted from a pre-trained neural network encoder equipped with an attention mechanism to address the image retrieval task for low-resolution image retrieval. Our method is simple yet effective since the contrastive learning framework drives similar samples close to each other in feature space by manipulating variants of their augmentations. To benchmark the proposed framework, we conducted quantitative and qualitative analyses of CARS196 (mAP = 0.8804), CUB200-2011 (mAP = 0.9379), and Stanford Online Products datasets (mAP = 0.9141) and analyzed their performances.

中文翻译：

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用注意力机制和对比学习重新审视低分辨率图像检索

最近的实证研究表明，深度神经网络学习的视觉表示可以成功地用作图像检索的描述符。一种常见的技术是利用预先训练的模型通过对损失进行排序和对标记数据进行微调来学习视觉描述符。然而，当查询分辨率低于训练图像的图像时，检索系统的性能显着下降。本研究考虑了一种对比学习框架，该框架对从预训练神经网络编码器提取的特征进行了微调，该编码器配备了注意机制，以解决低分辨率图像检索的图像检索任务。我们的方法简单而有效，因为对比学习框架通过操纵它们的增强变体来驱动特征空间中彼此靠近的相似样本。