Recently, most siamese network based trackers locate targets via object
classification and bounding-box regression. Generally, they select the
bounding-box with maximum classification confidence as the final prediction.
This strategy may miss the right result due to the accuracy misalignment
between classification and regression. In this paper, we propose a novel
siamese tracking algorithm called SiamRCR, addressing this problem with a
simple, light and effective solution. It builds reciprocal links between
classification and regression branches, which can dynamically re-weight their
losses for each positive sample. In addition, we add a localization branch to
predict the localization accuracy, so that it can work as the replacement of
the regression assistance link during inference. This branch makes the training
and inference more consistent. Extensive experimental results demonstrate the
effectiveness of SiamRCR and its superiority over the state-of-the-art
competitors on GOT-10k, LaSOT, TrackingNet, OTB-2015, VOT-2018 and VOT-2019.
Moreover, our SiamRCR runs at 65 FPS, far above the real-time requirement.

本文提出了一种名为 SiamRCR 的新型同孪网络追踪算法，建立了分类和回归分支之间的相互关联，通过添加定位分支来解决分类和回归不准确之间的问题，证明其在多个实验结果上具有优越性。

SiamRCR：针对视觉目标跟踪的互惠分类和回归

SiamRCR: Reciprocal Classification and Regression for Visual Object  Tracking

It is challenging for weakly supervised object detection network to precisely
predict the positions of the objects, since there are no instance-level
category annotations. Most existing methods tend to solve this problem by using
a two-phase learning procedure, i.e., multiple instance learning detector
followed by a fully supervised learning detector with bounding-box regression.
Based on our observation, this procedure may lead to local minima for some
object categories. In this paper, we propose to jointly train the two phases in
an end-to-end manner to tackle this problem. Specifically, we design a single
network with both multiple instance learning and bounding-box regression
branches that share the same backbone. Meanwhile, a guided attention module
using classification loss is added to the backbone for effectively extracting
the implicit location information in the features. Experimental results on
public datasets show that our method achieves state-of-the-art performance.

本论文提出一种通过端到端的方式联合训练多阶段模型来解决弱监督目标检测网络中的对象位置精确预测问题的方法，该方法引入了多例学习、包围框回归和分类损失引导的注意力模块等多种算法，实验结果表明该方法能够达到最佳的性能表现。

面向精确的端到端弱监督物体检测网络

Towards Precise End-to-end Weakly Supervised Object Detection Network

Bounding-box regression is a popular technique to refine or predict
localization boxes in recent object detection approaches. Typically,
bounding-box regressors are trained to regress from either region proposals or
fixed anchor boxes to nearby bounding boxes of a pre-defined target object
classes. This paper investigates whether the technique is generalizable to
unseen classes and is transferable to other tasks beyond supervised object
detection. To this end, we propose a class-agnostic and anchor-free box
regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a
bounding box of the nearest object from any given box. Trained on a relatively
small set of annotated images, UBBR successfully generalizes to unseen classes,
and can be used to improve localization in many vision problems. We demonstrate
its effectivenss on weakly supervised object detection and object discovery.

本文提出了一种通用的边界框回归器（UBBR），其中包括 bounding-box regression、object detection、generalizability 和 class-agnostic 等关键词，并展示了其在弱监督目标检测和目标发现中的有效性。

通用边界框回归及其应用

Universal Bounding Box Regression and Its Applications

In this paper we introduce a new method for text detection in natural images.
The method comprises two contributions: First, a fast and scalable engine to
generate synthetic images of text in clutter. This engine overlays synthetic
text to existing background images in a natural way, accounting for the local
3D scene geometry. Second, we use the synthetic images to train a
Fully-Convolutional Regression Network (FCRN) which efficiently performs text
detection and bounding-box regression at all locations and multiple scales in
an image. We discuss the relation of FCRN to the recently-introduced YOLO
detector, as well as other end-to-end object detection systems based on deep
learning. The resulting detection network significantly out performs current
methods for text detection in natural images, achieving an F-measure of 84.2%
on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per
second on a GPU.

介绍了一种新的自然图像中文本检测方法，它包括使用合成图像训练 Fully-Convolutional Regression Network (FCRN) 以高效执行所有位置和多个尺度的文本检测和边框回归，并且可以在 GPU 上处理 15 张图像 / 秒。