Scene text retrieval aims to localize and search all text instances from an
image gallery, which are the same or similar to a given query text. Such a task
is usually realized by matching a query text to the recognized words, outputted
by an end-to-end scene text spotter. In this paper, we address this problem by
directly learning a cross-modal similarity between a query text and each text
instance from natural images. Specifically, we establish an end-to-end
trainable network, jointly optimizing the procedures of scene text detection
and cross-modal similarity learning. In this way, scene text retrieval can be
simply performed by ranking the detected text instances with the learned
similarity. Experiments on three benchmark datasets demonstrate our method
consistently outperforms the state-of-the-art scene text spotting/retrieval
approaches. In particular, the proposed framework of joint detection and
similarity learning achieves significantly better performance than separated
methods. Code is available at: this https URL

本篇论文提出了一种新的场景文本检索方法，使用交叉模态相似性学习直接匹配查询文本和自然图像中每个文本实例之间的相似性，并通过建立端到端可训练的网络来实现场景文本检测和交叉模态相似性学习的联合优化，在三个基准数据集上的实验表明该方法优于现有的场景文本检测 / 检索方法，尤其是联合检测和相似性学习的框架比分离方法性能更好。

通过联合文本检测和相似性学习实现场景文本检索

Scene Text Retrieval via Joint Text Detection and Similarity Learning

Head and human detection have been rapidly improved with the development of
deep convolutional neural networks. However, these two tasks are often studied
separately without considering their inherent correlation, leading to that 1)
head detection is often trapped in more false positives, and 2) the performance
of human detector frequently drops dramatically in crowd scenes. To handle
these two issues, we present a novel joint head and human detection network,
namely JointDet, which effectively detects head and human body simultaneously.
Moreover, we design a head-body relationship discriminating module to perform
relational learning between heads and human bodies, and leverage this learned
relationship to regain the suppressed human detections and reduce head false
positives. To verify the effectiveness of the proposed method, we annotate head
bounding boxes of the CityPersons and Caltech-USA datasets, and conduct
extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets.
As a consequence, the proposed JointDet detector achieves state-of-the-art
performance on these three benchmarks. To facilitate further studies on the
head and human detection problem, all new annotations, source codes and trained
models will be public.

该研究提出了一种新的联合头部和人体检测网络，包括设计一个头体关系区分模块执行头和身体之间的关系学习，并利用所学关系来减少头部误报率和提高人体检测率。进行相关实验后，该联合检测器在三个基准数据集中取得了最佳性能，且提供新的注释，源代码和训练模型以便进一步研究。

联合头部和人体检测的关系学习

Relational Learning for Joint Head and Human Detection

We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.

本文提出一种基于双源深度卷积神经网络 (DS-CNN) 从单张图片中估计 2D 人体姿态的新方法，该方法集成了局部 (身体) 部分的外观和全局视角，实现了更准确的人体姿态估计，并结合多种图像处理算法，有效实现关节点检测、定位和人体姿态估计。