In person re-identification (ReID), very recent researches have validated
pre-training the models on unlabelled person images is much better than on
ImageNet. However, these researches directly apply the existing self-supervised
learning (SSL) methods designed for image classification to ReID without any
adaption in the framework. These SSL methods match the outputs of local views
(e.g., red T-shirt, blue shorts) to those of the global views at the same time,
losing lots of details. In this paper, we propose a ReID-specific pre-training
method, Part-Aware Self-Supervised pre-training (PASS), which can generate
part-level features to offer fine-grained information and is more suitable for
ReID. PASS divides the images into several local areas, and the local views
randomly cropped from each area are assigned with a specific learnable [PART]
token. On the other hand, the [PART]s of all local areas are also appended to
the global views. PASS learns to match the output of the local views and global
views on the same [PART]. That is, the learned [PART] of the local views from a
local area is only matched with the corresponding [PART] learned from the
global views. As a result, each [PART] can focus on a specific local area of
the image and extracts fine-grained information of this area. Experiments show
PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on
various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves
92.2\%/90.2\%/88.5\% mAP accuracy on Market1501 for supervised/UDA/USL ReID.
Our codes are available at this https URL

本文提出了一种面向个人再识别的预训练方法 PASS（Part-Aware Self-Supervised Pre-Training），可生成部分级特征提供细粒度信息，并在 Market1501 和 MSMT17 等任务上取得了最新的最优性能表现。

PASS: 面向人员再识别的部位感知自监督预训练

PASS: Part-Aware Self-Supervised Pre-Training for Person  Re-Identification

3D object detection from LiDAR point cloud is a challenging problem in 3D
scene understanding and has many practical applications. In this paper, we
extend our preliminary work PointRCNN to a novel and strong point-cloud-based
3D object detection framework, the part-aware and aggregation neural network
(Part-$A^2$ net). The whole framework consists of the part-aware stage and the
part-aggregation stage. Firstly, the part-aware stage for the first time fully
utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to
simultaneously predict high quality 3D proposals and accurate intra-object part
locations. The predicted intra-object part locations within the same proposal
are grouped by our new-designed RoI-aware point cloud pooling module, which
results in an effective representation to encode the geometry-specific features
of each 3D proposal. Then the part-aggregation stage learns to re-score the box
and refine the box location by exploring the spatial relationship of the pooled
intra-object part locations. Extensive experiments are conducted to demonstrate
the performance improvements from each component of our proposed framework. Our
Part-$A^2$ net outperforms all existing 3D detection methods and achieves new
state-of-the-art on KITTI 3D object detection dataset by utilizing only the
LiDAR point cloud data. Code is available at
this https URL

本文提出了一种基于 LiDAR 点云的强大的 Part-$A^2$ net 三维物体检测框架，该框架包含两个阶段：part-aware 和 part-aggregation。Part-$A^2$ net 能够通过该框架在 KITTI 3D 物体检测数据集上取得最新的最佳成果，而且只利用了 LiDAR 点云数据。