Few-shot learning (FSL) aims to classify images under low-data regimes, where
the conventional pooled global feature is likely to lose useful local
characteristics. Recent work has achieved promising performances by using deep
descriptors. They generally take all deep descriptors from neural networks into
consideration while ignoring that some of them are useless in classification
due to their limited receptive field, e.g., task-irrelevant descriptors could
be misleading and multiple aggregative descriptors from background clutter
could even overwhelm the object's presence. In this paper, we argue that a
Mutual Nearest Neighbor (MNN) relation should be established to explicitly
select the query descriptors that are most relevant to each task and discard
less relevant ones from aggregative clutters in FSL. Specifically, we propose
Discriminative Mutual Nearest Neighbor Neural Network (DMN4) for FSL. Extensive
experiments demonstrate that our method outperforms the existing
state-of-the-arts on both fine-grained and generalized datasets.

本文提出了一种用于 Few-shot learning 的 DMN4 模型，采用相互最近邻关系显式地选择与任务最相关的查询描述符并丢弃不相关的聚合噪声，通过广泛的实验验证，DMN4 模型在精细分类和广义数据集上均优于现有技术。

DMN4: 利用判别式相互最近邻神经网络进行少样本学习

DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor  Neural Network

This work addresses the problem of billion-scale nearest neighbor search. The
state-of-the-art retrieval systems for billion-scale databases are currently
based on the inverted multi-index, the recently proposed generalization of the
inverted index structure. The multi-index provides a very fine-grained
partition of the feature space that allows extracting concise and accurate
short-lists of candidates for the search queries. In this paper, we argue that
the potential of the simple inverted index was not fully exploited in previous
works and advocate its usage both for the highly-entangled deep descriptors and
relatively disentangled SIFT descriptors. We introduce a new retrieval system
that is based on the inverted index and outperforms the multi-index by a large
margin for the same memory consumption and construction complexity. For
example, our system achieves the state-of-the-art recall rates several times
faster on the dataset of one billion deep descriptors compared to the efficient
implementation of the inverted multi-index from the FAISS library.

本文提出一种新的基于倒排索引的检索系统，相比于现有的基于多重索引的系统，在相同内存消耗和构建复杂度下，在十亿级深度描述符数据集上，取得了数倍于现有最先进方法的检索性能。

重访用于亿级近似最近邻的倒排索引

Revisiting the Inverted Indices for Billion-Scale Approximate Nearest  Neighbors

This paper addresses the problem of large scale image retrieval, with the aim
of accurately ranking the similarity of a large number of images to a given
query image. To achieve this, we propose a novel Siamese network. This network
consists of two computational strands, each comprising of a CNN component
followed by a Fisher vector component. The CNN component produces dense, deep
convolutional descriptors that are then aggregated by the Fisher Vector method.
Crucially, we propose to simultaneously learn both the CNN filter weights and
Fisher Vector model parameters. This allows us to account for the evolving
distribution of deep descriptors over the course of the learning process. We
show that the proposed approach gives significant improvements over the
state-of-the-art methods on the Oxford and Paris image retrieval datasets.
Additionally, we provide a baseline performance measure for both these datasets
with the inclusion of 1 million distractors.

本文介绍了一种用于大规模图像检索的新型 Siamese 网络，结合了 CNN 组件和 Fisher vector 组件，可以同时学习 CNN 过滤器权重和 Fisher Vector 模型参数，并且结果比现有的方法有显著改善。

深度 Fisher-Vector 描述符连结网络用于图像检索

Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval

Deep convolutional neural network models pre-trained for the ImageNet
classification task have been successfully adopted to tasks in other domains,
such as texture description and object proposal generation, but these tasks
require annotations for images in the new domain. In this paper, we focus on a
novel and challenging task in the pure unsupervised setting: fine-grained image
retrieval. Even with image labels, fine-grained images are difficult to
classify, let alone the unsupervised retrieval task. We propose the Selective
Convolutional Descriptor Aggregation (SCDA) method. SCDA firstly localizes the
main object in fine-grained images, a step that discards the noisy background
and keeps useful deep descriptors. The selected descriptors are then aggregated
and dimensionality reduced into a short feature vector using the best practices
we found. SCDA is unsupervised, using no image label or bounding box
annotation. Experiments on six fine-grained datasets confirm the effectiveness
of SCDA for fine-grained image retrieval. Besides, visualization of the SCDA
features shows that they correspond to visual attributes (even subtle ones),
which might explain SCDA's high mean average precision in fine-grained
retrieval. Moreover, on general image retrieval datasets, SCDA achieves
comparable retrieval results with state-of-the-art general image retrieval
approaches.

本研究采用深层卷积神经网络处理复杂的图像应用场景，并使用无监督神经网络技术进行细节图像检索，该方法可以通过精细的图像特征聚合和维度降低来识别细节，而不需要过多标注信息。