CNN-based face detection methods have achieved significant progress in recent
years. In addition to the strong representation ability of CNN, post-processing
methods are also very important for the performance of face detection. In
general, the face detection method predicts several candidate bounding-boxes
for one face. NMS is used to filter out inaccurate candidate boxes to get the
most accurate box. The principle of NMS is to select the box with a higher
score as the basic box and then delete the box which has a large overlapping
area with the basic box but has a lower score. However, the current NMS method
and its improved versions do not perform well when face image quality is poor
or faces are in a cluster. In these situations, even after NMS filtering, there
is often a face corresponding to multiple predicted boxes. To reduce this kind
of negative result, in this paper, we propose a new NMS method that operates in
the reverse order of other NMS methods. Our method performs well on low-quality
and tiny face samples. Experiments demonstrate that our method is effective as
a post-processor for different face detection methods.

该论文介绍了一种新的基于 CNN 和 NMS 算法的人脸检测方法，可以有效处理低质量图像和小人脸，可以作为不同人脸检测方法的后处理器。

更精确、更整洁的人脸检测：反向非极大值抑制

Inverted Non-maximum Suppression for more Accurate and Neater Face  Detection

Visual object tracking is the problem of predicting a target object's state
in a video. Generally, bounding-boxes have been used to represent states, and a
surge of effort has been spent by the community to produce efficient causal
algorithms capable of locating targets with such representations. As the field
is moving towards binary segmentation masks to define objects more precisely,
in this paper we propose to extensively explore target-conditioned segmentation
methods available in the computer vision community, in order to transform any
bounding-box tracker into a segmentation tracker. Our analysis shows that such
methods allow trackers to compete with recently proposed segmentation trackers,
while performing quasi real-time.

本文通过研究计算机视觉领域中的目标条件分割方法，将边框跟踪器转化为分割跟踪器，从而实现了与最近提出的分割跟踪器竞争并具有准实时表现。

针对视觉目标跟踪器的目标条件分割方法探究

An Exploration of Target-Conditioned Segmentation Methods for Visual  Object Trackers

Training object class detectors typically requires a large set of images in
which objects are annotated by bounding-boxes. However, manually drawing
bounding-boxes is very time consuming. We propose a new scheme for training
object detectors which only requires annotators to verify bounding-boxes
produced automatically by the learning algorithm. Our scheme iterates between
re-training the detector, re-localizing objects in the training images, and
human verification. We use the verification signal both to improve re-training
and to reduce the search space for re-localisation, which makes these steps
different to what is normally done in a weakly supervised setting. Extensive
experiments on PASCAL VOC 2007 show that (1) using human verification to update
detectors and reduce the search space leads to the rapid production of
high-quality bounding-box annotations; (2) our scheme delivers detectors
performing almost as good as those trained in a fully supervised setting,
without ever drawing any bounding-box; (3) as the verification task is very
quick, our scheme substantially reduces total annotation time by a factor
6x-9x.

通过提出一种仅需要验证自动产生的边界框的方案来训练目标类检测器，这种方案可以通过反复训练检测器、在训练图像中重新定位对象并进行人类验证，从而使用验证信号来改善重新训练和减少重新定位的搜索空间。实验表明，采用人类验证更新检测器和减少搜索空间可以迅速产生高质量的边界框注释，而且与完全监督的检测器相比，可以在不绘制任何边界框的情况下提供几乎同样好的性能。