How important is it for training and evaluation sets to not have class
overlap in image retrieval? We revisit Google Landmarks v2 clean, the most
popular training set, by identifying and removing class overlap with Revisited
Oxford and Paris [34], the most popular evaluation set. By comparing the
original and the new RGLDv2-clean on a benchmark of reproduced state-of-the-art
methods, our findings are striking. Not only is there a dramatic drop in
performance, but it is inconsistent across methods, changing the ranking.What
does it take to focus on objects or interest and ignore background clutter when
indexing? Do we need to train an object detector and the representation
separately? Do we need location supervision? We introduce Single-stage
Detect-to-Retrieve (CiDeR), an end-to-end, single-stage pipeline to detect
objects of interest and extract a global image representation. We outperform
previous state-of-the-art on both existing training sets and the new
RGLDv2-clean. Our dataset is available at
this https URL

重叠类别的训练和评估集对于图像检索有多重要？我们重新审视了谷歌地标 v2 干净数据集，并通过移除与 Revisited Oxford 和 Paris 数据集的类别重叠来创建新的 RGLDv2-clean 数据集。通过对比原始数据集和新数据集在最先进方法基准测试中的表现，我们发现不仅性能大幅下降，而且不同方法的排名也发生变化。针对对象和背景干扰的索引问题，我们需要训练一个对象检测器和表示器分别还是同时进行？是否需要位置监督？我们引入了 Single-stage Detect-to-Retrieve（CiDeR）方法，一个端到端的单阶段流程，用于检测感兴趣的对象并提取全局图像表示。我们在现有训练集和新的 RGLDv2-clean 上均超过了先前最先进的方法。我们的数据集可以在提供的链接中获取。

关于图像检索中的训练测试类别重叠问题和检测

On Train-Test Class Overlap and Detection for Image Retrieval

While deep learning has become a key ingredient in the top performing methods
for many computer vision tasks, it has failed so far to bring similar
improvements to instance-level image retrieval. In this article, we argue that
reasons for the underwhelming results of deep methods on image retrieval are
threefold: i) noisy training data, ii) inappropriate deep architecture, and
iii) suboptimal training procedure. We address all three issues.
First, we leverage a large-scale but noisy landmark dataset and develop an
automatic cleaning method that produces a suitable training set for deep
retrieval. Second, we build on the recent R-MAC descriptor, show that it can be
interpreted as a deep and differentiable architecture, and present improvements
to enhance it. Last, we train this network with a siamese architecture that
combines three streams with a triplet loss. At the end of the training process,
the proposed architecture produces a global image representation in a single
forward pass that is well suited for image retrieval. Extensive experiments
show that our approach significantly outperforms previous retrieval approaches,
including state-of-the-art methods based on costly local descriptor indexing
and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively
report 94.7, 96.6, and 94.8 mean average precision. Our representations can
also be heavily compressed using product quantization with little loss in
accuracy. For additional material, please see
www.xrce.xerox.com/Deep-Image-Retrieval.

本文针对深度学习在实例级图像检索方面表现不佳的问题，提出了基于大规模嘈杂地标数据集的清洗方法、改进的 R-MAC 描述符、基于三元组损失的孪生网络，并在 Oxford 5k、Paris 6k 和 Holidays 数据集上，分别报告了 94.7，96.6 和 94.8 的平均精度，并通过产品量化处理可以实现图像表示的高度压缩。

图像检索的深度视觉表示端到端学习

End-to-end Learning of Deep Visual Representations for Image Retrieval

In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object / part detectors to build the correspondence between object
parts by using object or object part annotations inside training images. The
need for expensive object annotations prevents the wide usage of these methods.
Instead, we propose to select useful parts from multi-scale part proposals in
objects, and use them to compute a global image representation for
categorization. This is specially designed for the annotation-free fine-grained
categorization task, because useful parts have shown to play an important role
in existing annotation-dependent works but accurate part detectors can be
hardly acquired. With the proposed image representation, we can further detect
and visualize the key (most discriminative) parts in objects of different
classes. In the experiment, the proposed annotation-free method achieves better
accuracy than that of state-of-the-art annotation-free and most existing
annotation-dependent methods on two challenging datasets, which shows that it
is not always necessary to use accurate object / part annotations in
fine-grained image categorization.

该论文致力于通过使用多尺度部分提议来选择有用的部分，并使用它们计算全局图像表示以进行无注释的细粒度图像分类，同时探索了其关键部分的检测和可视化。实验表明该方法在两个具有挑战性的数据集上比同类方法更加准确。