In this paper, we propose a new query-based detection framework for crowd
detection. Previous query-based detectors suffer from two drawbacks: first,
multiple predictions will be inferred for a single object, typically in crowded
scenes; second, the performance saturates as the depth of the decoding stage
increases. Benefiting from the nature of the one-to-one label assignment rule,
we propose a progressive predicting method to address the above issues.
Specifically, we first select accepted queries prone to generate true positive
predictions, then refine the rest noisy queries according to the previously
accepted predictions. Experiments show that our method can significantly boost
the performance of query-based detectors in crowded scenes. Equipped with our
approach, Sparse RCNN achieves 92.0\% $\text{AP}$, 41.4\% $\text{MR}^{-2}$ and
83.2\% $\text{JI}$ on the challenging CrowdHuman \cite{shao2018crowdhuman}
dataset, outperforming the box-based method MIP \cite{chu2020detection} that
specifies in handling crowded scenarios. Moreover, the proposed method, robust
to crowdedness, can still obtain consistent improvements on moderately and
slightly crowded datasets like CityPersons \cite{zhang2017citypersons} and COCO
\cite{lin2014microsoft}. Code will be made publicly available at
this https URL

本论文提出了一种新的基于查询的检测框架，利用一对一标签分配规则，提出了一种渐进式预测方法来解决拥挤场景下已有检测器多次预测和性能饱和的问题；实验表明，这种方法可以显著提高拥挤场景下基于查询的检测器的性能并具有稳健性，可以在各种场景中实现一致的改进效果。

拥挤场景下逐步端到端目标检测

Progressive End-to-End Object Detection in Crowded Scenes

We propose a simple, intuitive yet powerful method for human-object
interaction (HOI) detection. HOIs are so diverse in spatial distribution in an
image that existing CNN-based methods face the following three major drawbacks;
they cannot leverage image-wide features due to CNN's locality, they rely on a
manually defined location-of-interest for the feature aggregation, which
sometimes does not cover contextually important regions, and they cannot help
but mix up the features for multiple HOI instances if they are located closely.
To overcome these drawbacks, we propose a transformer-based feature extractor,
in which an attention mechanism and query-based detection play key roles. The
attention mechanism is effective in aggregating contextually important
information image-wide, while the queries, which we design in such a way that
each query captures at most one human-object pair, can avoid mixing up the
features from multiple instances. This transformer-based feature extractor
produces so effective embeddings that the subsequent detection heads may be
fairly simple and intuitive. The extensive analysis reveals that the proposed
method successfully extracts contextually important features, and thus
outperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.7
mAP on V-COCO). The source codes are available at
$\href{this https URL}{\text{this https URL}}$.

提出了一种基于 transformer 的特征提取器，其中关注机制和基于查询的检测起着关键作用，可以避免多个 HOI 实例的特征混合，有效提取上下文重要特征，优于现有方法。