Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have …
P Xu, X Zhu, DA Clifton - IEEE Transactions on Pattern Analysis …, 2023 - ieeexplore.ieee.org
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
A Ando, S Gidaris, A Bursuc, G Puy… - Proceedings of the …, 2023 - openaccess.thecvf.com
Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, eg, via range projection, is an effective and popular approach. These projection-based methods …
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance. However, labeling the bounding boxes for the 3D …
Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …
Abstract We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that …
As a special type of transformer, vision transformers (ViTs) can be used for various computer vision (CV) applications. Convolutional neural networks (CNNs) have several potential …
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a …
CY Hong, YY Chou, TL Liu - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that …