We consider the problem of person search in unconstrained scene images.
Existing methods usually focus on improving the person detection accuracy to
mitigate negative effects imposed by misalignment, mis-detections, and false
alarms resulted from noisy people auto-detection. In contrast to previous
studies, we show that sufficiently reliable person instance cropping is
achievable by slightly improved state-of-the-art deep learning object detectors
(e.g. Faster-RCNN), and the under-studied multi-scale matching problem in
person search is a more severe barrier. In this work, we address this
multi-scale person search challenge by proposing a Cross-Level Semantic
Alignment (CLSA) deep learning approach capable of learning more discriminative
identity feature representations in a unified end-to-end model. This is
realised by exploiting the in-network feature pyramid structure of a deep
neural network enhanced by a novel cross pyramid-level semantic alignment loss
function. This favourably eliminates the need for constructing a
computationally expensive image pyramid and a complex multi-branch network
architecture. Extensive experiments show the modelling advantages and
performance superiority of CLSA over the state-of-the-art person search and
multi-scale matching methods on two large person search benchmarking datasets:
CUHK-SYSU and PRW.

本文提出了一种深度学习的交叉层语义对齐方法（CLSA），用于解决人物搜索中的多尺度匹配问题，并成功地减少了误差和计算量，该方法在两个大型人物搜索基准数据集（CUHK-SYSU 和 PRW）上性能卓越。

多尺度匹配的人员搜索

Person Search by Multi-Scale Matching

Community-based question answering (CQA) websites represent an important
source of information. As a result, the problem of matching the most valuable
answers to their corresponding questions has become an increasingly popular
research topic. We frame this task as a binary (relevant/irrelevant)
classification problem, and present an adversarial training framework to
alleviate label imbalance issue. We employ a generative model to iteratively
sample a subset of challenging negative samples to fool our classification
model. Both models are alternatively optimized using REINFORCE algorithm. The
proposed method is completely different from previous ones, where negative
samples in training set are directly used or uniformly down-sampled. Further,
we propose using Multi-scale Matching which explicitly inspects the correlation
between words and ngrams of different levels of granularity. We evaluate the
proposed method on SemEval 2016 and SemEval 2017 datasets and achieves
state-of-the-art or similar performance.

本研究提出了一种针对社区问答的二分类（相关 / 不相关）策略，采用敌对训练框架缓解标签失衡问题，并使用生成模型迭代地抽样一组具有挑战性的负样本，最终提高模型预测性能。此外，我们使用多尺度匹配方法显式地检查不同粒度级别的单词和 ngram 之间的相关性。在 SemEval 2016 和 SemEval 2017 数据集上进行评估，结果表明该方法达到或接近最佳性能。