Instance-level Image Retrieval (IIR), or simply Instance Retrieval, deals with the problem of finding all the images within an dataset that contain a query instance (e.g. an object). This paper makes the first attempt that tackles this problem using instance-discrimination based contrastive learning (CL). While CL has shown impressive performance for many computer vision tasks, the similar success has never been found in the field of IIR. In this work, we approach this problem by exploring the capability of deriving discriminative representations from pre-trained and fine-tuned CL models. To begin with, we investigate the efficacy of transfer learning in IIR, by comparing off-the-shelf features learned by a pre-trained deep neural network (DNN) classifier with features learned by a CL model. The findings inspired us to propose a new training strategy that optimizes CL towards learning IIR-oriented features, by using an Average Precision (AP) loss together with a fine-tuning method to learn contrastive feature representations that are tailored to IIR. Our empirical evaluation demonstrates significant performance enhancement over the off-the-shelf features learned from a pre-trained DNN classifier on the challenging Oxford and Paris datasets.
翻译:平时图像检索系统(IIR),即简单的平时图像检索检索系统(Rit Retreal),处理在包含查询实例(例如一个对象)的数据集中查找所有图像的问题。本文件首次尝试使用基于实例的差别化学习(CL)来解决这一问题。虽然CL为许多计算机的视觉任务展示了令人印象深刻的业绩,但在IIR领域却从未发现类似的成功。在这项工作中,我们通过探索从预先培训和微调的CL模型中产生歧视性表现的能力来处理这一问题。首先,我们通过比较预先培训的深神经网络(DNNN)分类所学的非现成特征和CL模型所学的特征来调查IR中转移学习的所有图像的功效。研究结果启发我们提出一项新的培训战略,通过使用平均精度损失和微调方法来学习适合IR的对比特征表现。我们的经验评价表明,在从具有挑战性的OFCSARG和前的DNFDG模型中学习的离世特征方面,显著提高了业绩。