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

Heavy occlusion and dense gathering in crowd scene make pedestrian detection
become a challenging problem, because it's difficult to guess a precise full
bounding box according to the invisible human part. To crack this nut, we
propose a mechanism called Visible Feature Guidance (VFG) for both training and
inference. During training, we adopt visible feature to regress the
simultaneous outputs of visible bounding box and full bounding box. Then we
perform NMS only on visible bounding boxes to achieve the best fitting full box
in inference. This manner can alleviate the incapable influence brought by NMS
in crowd scene and make full bounding box more precisely. Furthermore, in order
to ease feature association in the post application process, such as pedestrian
tracking, we apply Hungarian algorithm to associate parts for a human instance.
Our proposed method can stably bring about 2~3% improvements in mAP and AP50
for both two-stage and one-stage detector. It's also more effective for MR-2
especially with the stricter IoU. Experiments on Crowdhuman, Cityperson,
Caltech and KITTI datasets show that visible feature guidance can help detector
achieve promisingly better performances. Moreover, parts association produces a
strong benchmark on Crowdhuman for the vision community.

提出了一种称为 Visible Feature Guidance (VFG) 的机制，通过可见性特征对人行检测任务中的 Bounding Box 进行可视回归，使用 Hungarian algorithm 关联人体部位，能够提高人行检测的性能。

面向人群行人检测的可见特征引导

Visible Feature Guidance for Crowd Pedestrian Detection

Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will
potentially lead to a lower recall rate and a higher threshold introduces more
false positives. This problem is more severe in pedestrian detection because
the instance density varies more intensively. However, previous works on NMS
don't consider or vaguely consider the factor of the existent of nearby
pedestrians. Thus, we propose Nearby Objects Hallucinator (NOH), which
pinpoints the objects nearby each proposal with a Gaussian distribution,
together with NOH-NMS, which dynamically eases the suppression for the space
that might contain other objects with a high likelihood. Compared to
Greedy-NMS, our method, as the state-of-the-art, improves by $3.9\%$ AP,
$5.1\%$ Recall, and $0.8\%$ $\text{MR}^{-2}$ on CrowdHuman to $89.0\%$ AP and
$92.9\%$ Recall, and $43.9\%$ $\text{MR}^{-2}$ respectively.

该研究提出了一种名为 NOH 的方法，它可以通过确定每个提案附近的对象来改进 Greedy-NMS 算法，NOH-NMS 可以动态地减少可能包含其他对象的空间的抑制，并在行人检测领域 State-of-the-art 水平与传统算法相比，该方法分别提高了 3.9％的 AP，5.1％的召回率和 0.8％的 MR^-2。

NOH-NMS: 通过附近物体幻象改善行人检测

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination

We present a simple, fully-convolutional model for real-time (>30 fps)
instance segmentation that achieves competitive results on MS COCO evaluated on
a single Titan Xp, which is significantly faster than any previous
state-of-the-art approach. Moreover, we obtain this result after training on
only one GPU. We accomplish this by breaking instance segmentation into two
parallel subtasks: (1) generating a set of prototype masks and (2) predicting
per-instance mask coefficients. Then we produce instance masks by linearly
combining the prototypes with the mask coefficients. We find that because this
process doesn't depend on repooling, this approach produces very high-quality
masks and exhibits temporal stability for free. Furthermore, we analyze the
emergent behavior of our prototypes and show they learn to localize instances
on their own in a translation variant manner, despite being
fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster
replacement for standard NMS that only has a marginal performance penalty.
Finally, by incorporating deformable convolutions into the backbone network,
optimizing the prediction head with better anchor scales and aspect ratios, and
adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1
mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art
approaches while still running at real-time.

提出了一种实时实例分割的全卷积模型，通过生成原型掩模和预测实例掩模系数两个并行子任务，使用线性组合的方法得到实例掩模。通过使用改进的锚点尺度和纵横比进行优化，同时引入新的快速掩模回归分支以及快速 NMS 算法，使得该模型在实时情况下能够接近于最先进的方法。