@article{yan2023cross,title={Cross Modal Transformer via Coordinates Encoding for 3D Object Dectection},author={Yan, Junjie and Liu, Yingfei and Sun, Jianjian and Jia, Fan and Li, Shuailin and Wang, Tiancai and Zhang, Xiangyu},journal={arXiv preprint arXiv:2301.01283},year={2023}} ...
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...
环境调试——CMT:Cross Modal Transformer CMT 的官方源代码已经在github上发布。其源码也是基于 mmdet3d 框架。根据官方 README,笔者也测试了其在 nuscenes 上的精度,与论文所述一致。本文记录笔者将官方源代码跑通过程。新手小白可能不太会 mmdet3d。笔者来个保姆级教程。建议完整看完本文,再动手实践,而不是边看...
Subsequently, we combine language-specific Bidirectional Encoder Representations from Transformers with Wav2Vec2.0 audio features via a novel cascaded cross-modal transformer (CCMT). Our model is based on two cascaded transformer blocks. The first one combines text-specific features from distinct ...
将图像和文本的隐藏向量融合后送入交叉编码器。具体地说,利用线性投影层改变每个文本特征和图像特征的维数,使其保持一致。多层transformer通过cross attention融合两模态特征信息,产生最终的跨模态输出。 4 pretrain task 为了充分挖掘图像和文本对之间的匹配关系,设计了预排序+排序机制(个人理解类似向量化召回+精排的范式...
The cross-modal transformer can leverage self-attention and cross-modal attention to mine the modality-specific and complementary correlation. A bottleneck feature fusion is presented to obtain the compressed feature representation. To facilitate the network training, we further put forward a novel ...
几篇论文实现代码:《Cross-Modal Contrastive Learning for Text-to-Image Generation》(CVPR 2021) GitHub:https:// github.com/google-research/xmcgan_image_generation 《DANNet: A One-Stage Domain Adapt...
Next, we used the pre-processed tweet texts with two Transformer models, BERT [60] and RoBERTa [61], to populate the dataset further. BERT is used to generate embeddings of both tweet text and the supporting statements collected and cosine distance is computed with a high threshold of 0.9 ...
Description for Reasoning Process Concise Rationale Rationale Transformer (Marasović et al., 2020) Description for Reasoning Process Concise Rationale ViQAR (Dua et al., 2021) Description for Reasoning Process Chain-of-Thought ScienceQA (Lu et al., 2022) Description for Reasoning Process Chain-of...
个人主页:https://ustcmike.github.io/ 分享内容:自适应耦合Q-学习:通过反馈机制最小化估计偏差 耦合方法(ensemble method)是一种缓和Q-学习(Q-learning)过估计(overestimation)问题的方法。通常我们使用多个Q-函数估计器来估计函数值。众所周知,估计偏差在很大程度上取决于耦合大小(即目标中使用的 Q 函数估计器的...