We present Sparse R-CNN, a purely sparse method for object detection in
images. Existing works on object detection heavily rely on dense object
candidates, such as $k$ anchor boxes pre-defined on all grids of image feature
map of size $H\times W$. In our method, however, a fixed sparse set of learned
object proposals, total length of $N$, are provided to object recognition head
to perform classification and location. By eliminating $HWk$ (up to hundreds of
thousands) hand-designed object candidates to $N$ (e.g. 100) learnable
proposals, Sparse R-CNN completely avoids all efforts related to object
candidates design and many-to-one label assignment. More importantly, final
predictions are directly output without non-maximum suppression post-procedure.
Sparse R-CNN demonstrates accuracy, run-time and training convergence
performance on par with the well-established detector baselines on the
challenging COCO dataset, e.g., achieving 45.0 AP in standard $3\times$
training schedule and running at 22 fps using ResNet-50 FPN model. We hope our
work could inspire re-thinking the convention of dense prior in object
detectors. The code is available at: this https URL

Sparse R-CNN 是一种用于图像中目标检测的纯稀疏方法，通过固定的稀疏一组学习目标建议代替手动定义的物体候选框，并直接输出最终预测结果，表现优于基线模型，可用于 COCO 数据集等。

稀疏 R-CNN：可学习提议的端到端物体检测

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

We present a network architecture for processing point clouds that directly
operates on a collection of points represented as a sparse set of samples in a
high-dimensional lattice. Naively applying convolutions on this lattice scales
poorly, both in terms of memory and computational cost, as the size of the
lattice increases. Instead, our network uses sparse bilateral convolutional
layers as building blocks. These layers maintain efficiency by using indexing
structures to apply convolutions only on occupied parts of the lattice, and
allow flexible specifications of the lattice structure enabling hierarchical
and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both
point-based and image-based representations can be easily incorporated in a
network with such layers and the resulting model can be trained in an
end-to-end manner. We present results on 3D segmentation tasks where our
approach outperforms existing state-of-the-art techniques.

本文提出一种直接以高维网格中的稀疏采样点集合形式处理点云的网络架构，采用稀疏双边卷积层作为构建块，实现对网格的有效卷积计算，可以灵活地进行分层和空间感知特征学习，并且可以轻松地将基于点或基于图像的表示方法应用于网络中，实验证明本文的方法在三维分割任务中胜过现有的最先进技术。