3.4 Masked-Modal Training for Robustness 四、实验结果 论文链接:Cross Modal Transformer: Towards Fast and Robust 3D Object Detection 代码链接:github.com/junjie18/CMT 作者:Junjie Yan, Yingfei Liu, Jianjian Sun, Fan Jia, Shuailin Li, Tiancai Wang, Xiangyu Zhang 发表单位:旷视科技 会议/期刊:ICCV...
BEVFusion 是利用 lss 那一套,将 image feature 投影到 3D 坐标空间中;Transfusion 和 CMT 是利用 Transformer,得到 image 和 point feature 的 3D 编码。 整体流程上图展示了 CMT 的整体流程。结构很简单,在 img 和 points 的 token 分别加上对应的 positional encoder(两个 PE),再用位置 Query 去查询。
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...
操作简化：无需复杂的2D到3D转换，CMT仅通过基础操作就能达到当前的性能顶峰，表现出极高的效率和鲁棒性。多模态适应：即使没有激光雷达，CMT也能与视觉方法相当，展现其在不同条件下的适应性。论文中，作者对比了多模态3D目标检测的不同方法，如BEVFusion、Transfusion和CMT，后者通过Transformer架构实现图...
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 (...
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...
(TIP 2023) CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection @article{CAVER-TIP2023, author={Pang, Youwei and Zhao, Xiaoqi and Zhang, Lihe and Lu, Huchuan}, journal={IEEE Transactions on Image Processing}, title={CAVER: Cross-Modal View-Mixed Transformer for ...
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,...
[ICCV 2023] Cross Modal Transformer: Towards Fast and Robust 3D Object Detection - Woogie-Boogie/CMT