Lack of shape guidance and label jitter caused by information deficiency of
weak label are the main problems in 3D weakly-supervised object detection.
Current weakly-supervised models often use heuristics or assumptions methods to
infer information from weak labels without taking advantage of the inherent
clues of weakly-supervised and fully-supervised methods, thus it is difficult
to explore a method that combines data utilization efficiency and model
accuracy. In an attempt to address these issues, we propose a novel plug-and-in
point cloud feature representation network called Multi-scale Mixed Attention
(MMA). MMA utilizes adjacency attention within neighborhoods and disparity
attention at different density scales to build a feature representation
network. The smart feature representation obtained from MMA has shape tendency
and object existence area inference, which can constrain the region of the
detection boxes, thereby alleviating the problems caused by the information
default of weak labels. Extensive experiments show that in indoor weak label
scenarios, the fully-supervised network can perform close to that of the
weakly-supervised network merely through the improvement of point feature by
MMA. At the same time, MMA can turn waste into treasure, reversing the label
jitter problem that originally interfered with weakly-supervised detection into
the source of data enhancement, strengthening the performance of existing weak
supervision detection methods. Our code is available at
this https URL

我们提出了一个名为多尺度混合注意力（MMA）的新型插入式点云特征表示网络，通过使用邻域内的邻接注意力和不同密度尺度上的差异注意力构建特征表示网络，从而获得智能特征表示，并在室内弱标签场景中通过 MMA 对点特征的改进，使完全监督和弱监督网络的性能相近，并将标签抖动问题转化为数据增强的源泉，增强现有弱监督检测方法的性能。

智能特征是您所需

Smart Feature is What You Need

The key to a successful cascade architecture for precise instance
segmentation is to fully leverage the relationship between bounding box
detection and mask segmentation across multiple stages. Although modern
instance segmentation cascades achieve leading performance, they mainly make
use of a unidirectional relationship, i.e., mask segmentation can benefit from
iteratively refined bounding box detection. In this paper, we investigate an
alternative direction, i.e., how to take the advantage of precise mask
segmentation for bounding box detection in a cascade architecture. We propose a
Deeply Shape-guided Cascade (DSC) for instance segmentation, which iteratively
imposes the shape guidances extracted from mask prediction at the previous
stage on bounding box detection at current stage. It forms a bi-directional
relationship between the two tasks by introducing three key components: (1)
Initial shape guidance: A mask-supervised Region Proposal Network (mPRN) with
the ability to generate class-agnostic masks; (2) Explicit shape guidance: A
mask-guided region-of-interest (RoI) feature extractor, which employs mask
segmentation at previous stage to focus feature extraction at current stage
within a region aligned well with the shape of the instance-of-interest rather
than a rectangular RoI; (3) Implicit shape guidance: A feature fusion operation
which feeds intermediate mask features at previous stage to the bounding box
head at current stage. Experimental results show that DSC outperforms the
state-of-the-art instance segmentation cascade, Hybrid Task Cascade (HTC), by a
large margin and achieves 51.8 box AP and 45.5 mask AP on COCO test-dev. The
code is released at: this https URL

本文提出了一种深度形状引导级联算法 (DSC)，通过引入三种关键组件，即初始形状指导、显式形状指导和隐式形状指导，使掩模分割和边界框检测形成双向关系，进而提高了实例分割的性能。实验结果表明，DSC 较当前最先进的实例分割算法 Hybrid Task Cascade (HTC) 提升明显，在 COCO 测试中获得了 51.8 的 box AP 和 45.5 的 mask AP。