Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained. In light of this situation, this work looks into a different approach which has not been well investigated for instance image retrieval previously: {can we learn feature representation \textit{specific to} a given retrieval task in order to achieve excellent retrieval?} Our finding is encouraging. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. This representation can be made even more effective by boosting it with image similarity information mined from the dataset. As experimentally validated, such a simple ``self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods. Ablation study is conducted to show the appealing properties of this approach and its limitation on generalisation across datasets.
翻译:质量特征代表是实例图像检索的关键。 要实现这一目标, 现有方法通常会采用在基准数据集上预先培训的深型模型, 或甚至将模型微调为基于任务的标签辅助数据集。 虽然取得了有希望的结果, 但这种方法受到两个问题的限制:(1) 基准数据集与特定检索任务数据集的数据集之间的域间差距;(2) 所需的辅助数据集无法轻易获得。 鉴于这种情况, 这项工作会看到一种不同的方法, 先前对图像检索没有很好地进行调查 : {我们能否学习一个特定功能代表\ textit{ 来完成一个特定检索任务?} } 我们的发现是令人鼓舞的。 通过添加一个对象建议生成图像区域供自我监督学习, 被调查的方法可以成功学习特定数据集的域间的具体特征代表 ; (2) 所需的辅助数据集无法轻易获得。 通过用从数据集中提取的类似图像信息来提升它, 这样简单的“ 自我超级化学习+ 自我增强自我定位 ” 的方法可以与相关的州级检索方法竞争, 显示其总体检索方法。