This paper shows that Masking the Deep hierarchical features is an efficient
self-supervised method, denoted as MaskDeep. MaskDeep treats each patch in the
representation space as an independent instance. We mask part of patches in the
representation space and then utilize sparse visible patches to reconstruct
high semantic image representation. The intuition of MaskDeep lies in the fact
that models can reason from sparse visible patches semantic to the global
semantic of the image. We further propose three designs in our framework: 1) a
Hierarchical Deep-Masking module to concern the hierarchical property of patch
representations, 2) a multi-group strategy to improve the efficiency without
any extra computing consumption of the encoder and 3) a multi-target strategy
to provide more description of the global semantic. Our MaskDeep brings decent
improvements. Trained on ResNet50 with 200 epochs, MaskDeep achieves
state-of-the-art results of 71.2% Top1 accuracy linear classification on
ImageNet. On COCO object detection tasks, MaskDeep outperforms the
self-supervised method SoCo, which specifically designed for object detection.
When trained with 100 epochs, MaskDeep achieves 69.6% Top1 accuracy, which
surpasses current methods trained with 200 epochs, such as HCSC, by 0.4% .

本研究提出了一种有效的自监督方法 MaskDeep，通过层次化地掩盖图像中的部分表示来重构高语义信息，通过固定可见区域，能够提供图像的更多描述，实验结果表明 MaskDeep 超越了多项自监督方法，成为了目前图像线性分类和对象检测任务最佳表现。

层次特征掩模的自监督学习

Mask Hierarchical Features For Self-Supervised Learning

Recognizing and localizing objects in the 3D space is a crucial ability for
an AI agent to perceive its surrounding environment. While significant progress
has been achieved with expensive LiDAR point clouds, it poses a great challenge
for 3D object detection given only a monocular image. While there exist
different alternatives for tackling this problem, it is found that they are
either equipped with heavy networks to fuse RGB and depth information or
empirically ineffective to process millions of pseudo-LiDAR points. With
in-depth examination, we realize that these limitations are rooted in
inaccurate object localization. In this paper, we propose a novel and
lightweight approach, dubbed {\em Progressive Coordinate Transforms} (PCT) to
facilitate learning coordinate representations. Specifically, a localization
boosting mechanism with confidence-aware loss is introduced to progressively
refine the localization prediction. In addition, semantic image representation
is also exploited to compensate for the usage of patch proposals. Despite being
lightweight and simple, our strategy leads to superior improvements on the
KITTI and Waymo Open Dataset monocular 3D detection benchmarks. At the same
time, our proposed PCT shows great generalization to most coordinate-based 3D
detection frameworks. The code is available at:
this https URL .

本文提出一种名为 “Progressive Coordinate Transforms”（PCT）的新型简单的轻量级方法来促进学习坐标表示，通过引入具有自信损失的定位增强机制来逐步改进定位预测，并利用语义图像表示来弥补局部提案的使用，得到了在 KITTI 和 Waymo Open Dataset 单眼 3D 检测基准上具有卓越的改进并具有良好的泛化性能。