Visual geolocalization is a cost-effective and scalable task that involves
matching one or more query images, taken at some unknown location, to a set of
geo-tagged reference images. Existing methods, devoted to semantic features
representation, evolving towards robustness to a wide variety between query and
reference, including illumination and viewpoint changes, as well as scale and
seasonal variations. However, practical visual geolocalization approaches need
to be robust in appearance changing and extreme viewpoint variation conditions,
while providing accurate global location estimates. Therefore, inspired by
curriculum design, human learn general knowledge first and then delve into
professional expertise. We first recognize semantic scene and then measure
geometric structure. Our approach, termed CurriculumLoc, involves a delicate
design of multi-stage refinement pipeline and a novel keypoint detection and
description with global semantic awareness and local geometric verification. We
rerank candidates and solve a particular cross-domain perspective-n-point (PnP)
problem based on these keypoints and corresponding descriptors, position
refinement occurs incrementally. The extensive experimental results on our
collected dataset, TerraTrack and a benchmark dataset, ALTO, demonstrate that
our approach results in the aforementioned desirable characteristics of a
practical visual geolocalization solution. Additionally, we achieve new high
recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances
metrics, respectively. Dataset, code and trained models are publicly available
on this https URL

视觉地理定位方法的多阶段课程学习以及全局和局部特征的关键点检测、描述和位置调整使其成为一种实用的视觉地理定位解决方案，取得了高召回率的好成绩。

CurriculumLoc: 通过多阶段改进增强跨领域地理定位

CurriculumLoc: Enhancing Cross-Domain Geolocalization through  Multi-Stage Refinement

We present a novel 3D object detection framework, named IPOD, based on raw
point cloud. It seeds object proposal for each point, which is the basic
element. This paradigm provides us with high recall and high fidelity of
information, leading to a suitable way to process point cloud data. We design
an end-to-end trainable architecture, where features of all points within a
proposal are extracted from the backbone network and achieve a proposal feature
for final bounding inference. These features with both context information and
precise point cloud coordinates yield improved performance. We conduct
experiments on KITTI dataset, evaluating our performance in terms of 3D object
detection, Bird's Eye View (BEV) detection and 2D object detection. Our method
accomplishes new state-of-the-art , showing great advantage on the hard set.

本文提出了一种基于点云的新型三维物体检测框架 IPOD，使用每个点作为基本部件提供了高召回率和高信息保真度，设计了一种全尺寸可训练的架构，在 KITTI 数据集上取得了最新的最好表现，特别是在 hard set 上显示出极大的优势，可用于三维、俯视图和二维物体检测。

IPOD: 点云强化点型物体探测器

IPOD: Intensive Point-based Object Detector for Point Cloud

A number of classification problems need to deal with data imbalance between
classes. Often it is desired to have a high recall on the minority class while
maintaining a high precision on the majority class. In this paper, we review a
number of resampling techniques proposed in literature to handle unbalanced
datasets and study their effect on classification performance.

研究分类问题中遇到的数据不平衡的情况，讨论了文献中提出的一些重新采样技术，并研究了它们对分类性能的影响。