One technique to improve the retrieval effectiveness of a search engine is to
expand documents with terms that are related or representative of the
documents' content.From the perspective of a question answering system, this
might comprise questions the document can potentially answer. Following this
observation, we propose a simple method that predicts which queries will be
issued for a given document and then expands it with those predictions with a
vanilla sequence-to-sequence model, trained using datasets consisting of pairs
of query and relevant documents. By combining our method with a
highly-effective re-ranking component, we achieve the state of the art in two
retrieval tasks. In a latency-critical regime, retrieval results alone (without
re-ranking) approach the effectiveness of more computationally expensive neural
re-rankers but are much faster.

该研究提出了一种将搜索引擎文档扩展为与其内容相关或代表性术语的简单方法，并利用基于 Vanilla 序列到序列模型的数据集对其进行扩展，结合高效的重新排序组件，实现了两种检索任务的最高效结果。

查询预测扩展文档

Document Expansion by Query Prediction

Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
The code is available online at:
this https URL

本文提出了一种有效的人物再识别方法，结合了 fine 和 coarse pose 信息的卷积神经网络，同时提出了一种新的无监督自动重排架构，取得了优异的效果。

一种姿态敏感的个体再识别嵌入式表征方法：扩展的交叉领域再排序

A Pose-Sensitive Embedding for Person Re-Identification with Expanded  Cross Neighborhood Re-Ranking

As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.

该研究提出了一种用于人员再识别的 “分治与利用” 重排序框架，它可以利用从高维特征向量不同部分中提取的多样性进行融合排序。

分割与融合：一种用于人员再识别的重新排名方法

Divide and Fuse: A Re-ranking Approach for Person Re-identification

When considering person re-identification (re-ID) as a retrieval process,
re-ranking is a critical step to improve its accuracy. Yet in the re-ID
community, limited effort has been devoted to re-ranking, especially those
fully automatic, unsupervised solutions. In this paper, we propose a
k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is
that if a gallery image is similar to the probe in the k-reciprocal nearest
neighbors, it is more likely to be a true match. Specifically, given an image,
a k-reciprocal feature is calculated by encoding its k-reciprocal nearest
neighbors into a single vector, which is used for re-ranking under the Jaccard
distance. The final distance is computed as the combination of the original
distance and the Jaccard distance. Our re-ranking method does not require any
human interaction or any labeled data, so it is applicable to large-scale
datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW
datasets confirm the effectiveness of our method.

本研究提出一种基于 k-reciprocal 编码和 Jaccard 距离的自动化重新排名方法，在人员再识别方面取得了好的效果，并且可以适用于大规模数据集。

k - 相似编码重新排序的人物再识别

Re-ranking Person Re-identification with k-reciprocal Encoding

Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets.

本文尝试使用基于 CNN 的图像表示来重新设计目标检索流程，首次在 Oxford5k 和 Paris6k 数据集上效果能够与传统方法一较高下。