Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

While originally designed for natural language processing tasks, the self-attention
mechanism has recently taken various computer vision areas by storm. However, the 2D …

Self-attention networks have revolutionized natural language processing and are making
impressive strides in image analysis tasks such as image classification and object detection …

The irregular domain and lack of ordering make it challenging to design deep neural
networks for point cloud processing. This paper presents a novel framework named Point …

In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a
Transformer-based image classifier equipped with co-scale and conv-attentional …

Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …

Abstract We introduce the SE (3)-Transformer, a variant of the self-attention module for 3D
point-clouds, which is equivariant under continuous 3D roto-translations. Equivariance is …

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D
sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network …