在本文,提出了 Cross-Modal Transformer (CMT),这是一种简单但有效的端到端管道,用于鲁棒3D 对象检测。 首先,提出了坐标编码模块(CEM),它通过将 3D 点集隐式编码为多模态标记来生成位置感知特征。具体来说,对于相机图像,从视锥体空间采样的 3D 点用于指示每个像素的 3D 位置的概率。而对于 LiDAR,BEV 坐标只是...
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection ICCV 2023 在本文中,我们提出了 Cross-Modal Transformer (CMT),这是一种简单而有效的端到端管道,用于鲁棒的 3D 对象检测(见图 1(c))。首先,我们提出了坐标编码模块(CEM),它通过将 3D 点集隐式编码为多模态标记来生成位置感知特征。具体...
论文解读——CMT:Cross Modal Transformer 论文解读——CMT:Cross Modal Transformer CMT 是旷视在 ICCV2023 的一篇论文,其基于 PERT,并加入了激光雷达数据,利用 Transformer 很好地融合了两种模态的数据。新手小白建议先看 DETR 系列。论文的继承关系为:CMT -> PETR -> DETR3D -> Deformable DETR -> DETR。可以...
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection Junjie Yan Yingfei Liu ✉ Jianjian Sun Fan Jia Tiancai Wang Xiangyu Zhang MEGVII Technology Shuailin Li Abstract In this paper, we propose a robust 3D detector, named Cross Modal Transformer (...
In order to better combine the two modalities, we propose a novel Cross-Modal Transformer for human action recognition鈥擟MF-Transformer, which effectively fuses two different modalities. In spatio-temporal modality, video frames are used as inputs and directional attention is used in the ...
CMX(Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers)是一种利用Transformer模型实现跨模态融合的方法,旨在提高RGB-X(其中X代表其他模态数据,如深度图、红外图像等)语义分割任务的性能。CMX通过融合来自不同模态的信息,使模型能够更全面地理解场景,从而提升分割的准确性和鲁棒性。 2. 阐述cross-...
We propose a cross-modal transformer-based neural correction models that refines the output of an automatic speech recognition (ASR) system so as to exclude ASR errors. Generally, neural correction models are composed of encoder-decoder networks, which can directly model sequence-to-sequence mapping...
Dance Style Transfer with Cross-modal Transformer Wenjie Yin*, Hang Yin*, Kim Baraka†, Danica Kragic*, and Ma˚rten Bjo¨rkman* *KTH Royal Institute of Technology, Stockholm, Sweden †Vrije Universiteit Amsterdam, Amsterdam, Netherlands yinw@kth.se, hyin@kth.se, k.baraka@vu.nl,...
FFM:feature fusion module 结构如下图所示,可以看出,是基于 Transformer 的。和其他方法不同的是,这里把两个模态对等处理了。只不过在QKV计算上,使用了《Efficient Attention: Attention with Linear Complexities》里的处是方法,可以降低attention的计算量。在FFN部分,采用了Depth-wise conv取代MLP,同时,残差连接添加...
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection CMT_nuScenes_testset.mp4 This repository is an official implementation of CMT. Performance comparison between CMT and existing methods. All speed statistics are measured on a single Tesla A100 GPU using the best model of official...