因此,在这项工作中,我们假设hypothesize将CNN强大高效的局部建模能力与Transformers卓越的全局特征学习能力相结合,可以提高3D点云分类的准确性和效率。 因此,我们开发了一种新的点云分类架构,称为3D Convolution-Transformer Network(3DCTN),将卷积合并到Transformer中,使其内在高效,并使用最先进的分类方法获得具有竞争力的...
The architecture of the proposed AI model for early smoke detection is shown in Fig.2. Starting with a backbone network, the network is split into two parallel paths. One path is configured with the ViT blocks and the other one with CNN blocks. Both CNN and ViT have their own advantages ...
https://github.com/hi-zhengcheng/sctngithub.com/hi-zhengcheng/sctn 1. Motivation 这篇文章是在FLOT的基础上做的。理解SCTN之前,先大概回顾一下FLOT的主要流程。 FLOT首先使用一个类似于PointNet++的网络,计算出每个点的feature。预测scene flow时,主要是在feature空间内,使用Optimal transport算法在两个点...
Forests are invaluable resources, and fire is a natural process that is considered an integral part of the forest ecosystem. Although fire offers several ecological benefits, its frequent occurrence in different parts of the world has raised concerns in
22 p. PERSE: Personalized 3D Generative Avatars from A Single Portrait 15 p. Action-Agnostic Point-Level Supervision for Temporal Action Detection 8 p. Branes Screening Quarks and Defect Operators 84 p. SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Su...
Results: With only 0.6 million parameters and 0.4 billion floating point operations per second, the hybrid network of convolutional and vision transformer blocks efficiently detects smoke in normal and foggy environmental conditions. It outperforms seven state-of-the-art methods o...
Sparse Convolution-Transformer Network (SCTN): A Scene Flow Estimation Solution SCTN, presented at the 2022 AAAI conference, advances the state-of-the-art in scene flow prediction for 3D point clouds. Departing from the FLOT method, SCTN focuses on enhancing feature extraction and ...
Although convolutional networks (ConvNets) have enjoyed great success in computer vision (CV), it suffers from capturing global information crucial to dense prediction tasks such as object detection and segmentation. In this work, we innovatively propose ConTNet (ConvolutionTransformer Network), combining...
Second, most of these models adopt the image-wise classification network based on “encoder–label” structure, which make predictions through the features of the last stage and cannot make full use of the information obtained from the other stages. As a result, the “encoder–label” structure...
The spatial and spectral features extracted by CNN are generally combined by simple addition or connection at the end of the network, which may weaken the integration of these features. In short, incomplete feature extraction, inappropriate feature fusion, and high time consumption limit the ...