Person search aims to simultaneously localize and identify a query person
from realistic, uncropped images. To achieve this goal, state-of-the-art models
typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing
to the ROI-Align operation, this pipeline yields promising accuracy as re-id
features are explicitly aligned with the corresponding object regions, but in
the meantime, it introduces high computational overhead due to dense object
anchors. In this work, we present an anchor-free approach to efficiently
tackling this challenging task, by introducing the following dedicated designs.
First, we select an anchor-free detector (i.e., FCOS) as the prototype of our
framework. Due to the lack of dense object anchors, it exhibits significantly
higher efficiency compared with existing person search models. Second, when
directly accommodating this anchor-free detector for person search, there exist
several major challenges in learning robust re-id features, which we summarize
as the misalignment issues in different levels (i.e., scale, region, and task).
To address these issues, we propose an aligned feature aggregation module to
generate more discriminative and robust feature embeddings. Accordingly, we
name our model as Feature-Aligned Person Search Network (AlignPS). Third, by
investigating the advantages of both anchor-based and anchor-free models, we
further augment AlignPS with an ROI-Align head, which significantly improves
the robustness of re-id features while still keeping our model highly
efficient. Extensive experiments conducted on two challenging benchmarks (i.e.,
CUHK-SYSU and PRW) demonstrate that our framework achieves state-of-the-art or
competitive performance, while displaying higher efficiency. All the source
codes, data, and trained models are available at:
this https URL

本研究提出一种基于 AlignPS 框架的人物检索网络，采用 anchor-free 检测器，引入对齐特征聚合模块来生成更具辨别力和鲁棒性的 re-id 特征嵌入，同时并将 ROI-Align head 集成到模型中，该模型在 CUHK-SYSU 和 PRW 数据集上实验，显示出与最新模型相媲美的性能，并具有更高的效率。

高效的人物搜尋：一種無錨定方法

Efficient Person Search: An Anchor-Free Approach

Person search aims to simultaneously localize and identify a query person
from realistic, uncropped images, which can be regarded as the unified task of
pedestrian detection and person re-identification (re-id). Most existing works
employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but
with high computational overhead. In this work, we present the Feature-Aligned
Person Search Network (AlignPS), the first anchor-free framework to efficiently
tackle this challenging task. AlignPS explicitly addresses the major
challenges, which we summarize as the misalignment issues in different levels
(i.e., scale, region, and task), when accommodating an anchor-free detector for
this task. More specifically, we propose an aligned feature aggregation module
to generate more discriminative and robust feature embeddings by following a
"re-id first" principle. Such a simple design directly improves the baseline
anchor-free model on CUHK-SYSU by more than 20% in mAP. Moreover, AlignPS
outperforms state-of-the-art two-stage methods, with a higher speed. Code is
available at this https URL

本文提出了一种基于 AlignPS 的 anchor-free 框架来解决人物搜索问题，包括行人检测和人物重新识别，通过对齐特征聚合模块，实现更鲁棒的特征嵌入，显著提高了基线 anchor-free 模型的性能。