Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make
contribution to the task of 3D object detection in different ways. Existing basic components in …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Abstract We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Convolution exploits locality for efficiency at a cost of missing long range context. Self-
attention has been adopted to augment CNNs with non-local interactions. Recent works …

Semantic, instance, and panoptic segmentations have been addressed using different and
specialized frameworks despite their underlying connections. This paper presents a unified …

We propose a new convolution called Dynamic Region-Aware Convolution (DRConv),
which can automatically assign multiple filters to corresponding spatial regions where …

Abstract The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it …

Dynamic networks have shown their promising capability in reducing theoretical
computation complexity by adapting their architectures to the input during inference …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …