Extending the success of 2D Large Kernel to 3D perception is challenging due
to: 1. the cubically-increasing overhead in processing 3D data; 2. the
optimization difficulties from data scarcity and sparsity. Previous work has
taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by
introducing block-shared weights. However, to reduce the feature variations
within a block, it only employs modest block size and fails to achieve larger
kernels like the 21x21x21. To address this issue, we propose a new method,
called LinK, to achieve a wider-range perception receptive field in a
convolution-like manner with two core designs. The first is to replace the
static kernel matrix with a linear kernel generator, which adaptively provides
weights only for non-empty voxels. The second is to reuse the pre-computed
aggregation results in the overlapped blocks to reduce computation complexity.
The proposed method successfully enables each voxel to perceive context within
a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D
object detection and 3D semantic segmentation, demonstrate the effectiveness of
our method. Notably, we rank 1st on the public leaderboard of the 3D detection
benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based
backbone into the basic detector, CenterPoint. We also boost the strong
segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is
available at this https URL.

提出了一种名为 LinK 的方法，通过使用线性卷积核生成器和重复利用重叠块中预先计算的聚合结果来扩展感受野，从而成功实现每个体素在 21x21x21 范围内感知上下文，并在三维物体检测和语义分割任务中取得了显着表现。

LinK: 基于激光雷达的三维感知的线性核

LinK: Linear Kernel for LiDAR-based 3D Perception

When designing Convolutional Neural Networks (CNNs), one must select the
size\break of the convolutional kernels before training. Recent works show CNNs
benefit from different kernel sizes at different layers, but exploring all
possible combinations is unfeasible in practice. A more efficient approach is
to learn the kernel size during training. However, existing works that learn
the kernel size have a limited bandwidth. These approaches scale kernels by
dilation, and thus the detail they can describe is limited. In this work, we
propose FlexConv, a novel convolutional operation with which high bandwidth
convolutional kernels of learnable kernel size can be learned at a fixed
parameter cost. FlexNets model long-term dependencies without the use of
pooling, achieve state-of-the-art performance on several sequential datasets,
outperform recent works with learned kernel sizes, and are competitive with
much deeper ResNets on image benchmark datasets. Additionally, FlexNets can be
deployed at higher resolutions than those seen during training. To avoid
aliasing, we propose a novel kernel parameterization with which the frequency
of the kernels can be analytically controlled. Our novel kernel
parameterization shows higher descriptive power and faster convergence speed
than existing parameterizations. This leads to important improvements in
classification accuracy.

该论文提出了一种名为 FlexConv 的新型卷积操作，可以在学习可学习核大小的高带宽卷积核时以固定的参数成本来建模长期依赖性，提高分类准确性。

FlexConv：可微卷积，连续改变核大小

FlexConv: Continuous Kernel Convolutions with Differentiable Kernel  Sizes

Winograd's minimal filtering algorithm has been widely used in Convolutional
Neural Networks (CNNs) to reduce the number of multiplications for faster
processing. However, it is only effective on convolutions with kernel size as
3x3 and stride as 1, because it suffers from significantly increased FLOPs and
numerical accuracy problem for kernel size larger than 3x3 and fails on
convolution with stride larger than 1. In this paper, we propose a novel
Decomposable Winograd Method (DWM), which breaks through the limitation of
original Winograd's minimal filtering algorithm to a wide and general
convolutions. DWM decomposes kernels with large size or large stride to several
small kernels with stride as 1 for further applying Winograd method, so that
DWM can reduce the number of multiplications while keeping the numerical
accuracy. It enables the fast exploring of larger kernel size and larger stride
value in CNNs for high performance and accuracy and even the potential for new
CNNs. Comparing against the original Winograd, the proposed DWM is able to
support all kinds of convolutions with a speedup of ~2, without affecting the
numerical accuracy.

本文提出了一种新的分解 Winograd 方法（DWM），可以显著提高 Winograd 算法在卷积神经网络中的应用范围（包括大尺寸核和大步长卷积），从而在保持数值精度的同时，减少计算量和提高性能。

DWM：一种可分解的 Winograd 卷积加速方法

DWM: A Decomposable Winograd Method for Convolution Acceleration

Depthwise convolution is becoming increasingly popular in modern efficient
ConvNets, but its kernel size is often overlooked. In this paper, we
systematically study the impact of different kernel sizes, and observe that
combining the benefits of multiple kernel sizes can lead to better accuracy and
efficiency. Based on this observation, we propose a new mixed depthwise
convolution (MixConv), which naturally mixes up multiple kernel sizes in a
single convolution. As a simple drop-in replacement of vanilla depthwise
convolution, our MixConv improves the accuracy and efficiency for existing
MobileNets on both ImageNet classification and COCO object detection. To
demonstrate the effectiveness of MixConv, we integrate it into AutoML search
space and develop a new family of models, named as MixNets, which outperform
previous mobile models including MobileNetV2 [20] (ImageNet top-1 accuracy
+4.2%), ShuffleNetV2 [16] (+3.5%), MnasNet [26] (+1.3%), ProxylessNAS [2]
(+2.2%), and FBNet [27] (+2.0%). In particular, our MixNet-L achieves a new
state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings
(<600M FLOPS). Code is at this https URL
tensorflow/tpu/tree/master/models/official/mnasnet/mixnet

本文系统地研究了不同核大小的影响，提出了一种新的混合深度卷积（MixConv），将多个核大小自然混合在单个卷积中，为现有的 MobileNets 在 ImageNet 分类和 COCO 目标检测中提高了准确性和效率。