Pedestrian detection has significantly progressed in recent years, thanks to
the development of DNNs. However, detection performance at occluded scenes is
still far from satisfactory, as occlusion increases the intra-class variance of
pedestrians, hindering the model from finding an accurate classification
boundary between pedestrians and background clutters. From the perspective of
reducing intra-class variance, we propose to complete features for occluded
regions so as to align the features of pedestrians across different occlusion
patterns. An important premise for feature completion is to locate occluded
regions. From our analysis, channel features of different pedestrian proposals
only show high correlation values at visible parts and thus feature
correlations can be used to model occlusion patterns. In order to narrow down
the gap between completed features and real fully visible ones, we propose an
adversarial learning method, which completes occluded features with a generator
such that they can hardly be distinguished by the discriminator from real fully
visible features. We report experimental results on the CityPersons, Caltech
and CrowdHuman datasets. On CityPersons, we show significant improvements over
five different baseline detectors, especially on the heavy occlusion subset.
Furthermore, we show that our proposed method FeatComp++ achieves
state-of-the-art results on all the above three datasets without relying on
extra cues.

我们提出了一种基于特征补全和对抗学习的行人检测方法，通过对被遮挡区域进行特征补全，实现了行人特征的对齐，实验证明该方法在重度遮挡场景下取得了显著改进，并在多个数据集上达到了最先进的结果。

想象看见：通过对抗特征补全实现被遮挡行人检测

Imagine the Unseen: Occluded Pedestrian Detection via Adversarial  Feature Completion

Human mesh reconstruction from a single image is challenging in the presence
of occlusion, which can be caused by self, objects, or other humans. Existing
methods either fail to separate human features accurately or lack proper
supervision for feature completion. In this paper, we propose Dense Inpainting
Human Mesh Recovery (DIMR), a two-stage method that leverages dense
correspondence maps to handle occlusion. Our method utilizes a dense
correspondence map to separate visible human features and completes human
features on a structured UV map dense human with an attention-based feature
completion module. We also design a feature inpainting training procedure that
guides the network to learn from unoccluded features. We evaluate our method on
several datasets and demonstrate its superior performance under heavily
occluded scenarios compared to other methods. Extensive experiments show that
our method obviously outperforms prior SOTA methods on heavily occluded images
and achieves comparable results on the standard benchmarks (3DPW).

我们提出了一种名为 Dense Inpainting Human Mesh Recovery (DIMR) 的两阶段方法，利用密集的对应关系图处理遮挡问题，采用基于注意力机制的特征完成模块，通过特征修补训练过程指导网络从非遮挡特征中学习，实验证明我们的方法在重度遮挡场景下表现优异，并在标准基准（3DPW）上取得可比较的结果。