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 ...
【ARXIV2203】CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 高峰OUC 中国海洋大学,计算机学院教师7 人赞同了该文章 1、研究动机 当前的语义分割主要利用RGB图像,加入多源信息作为辅助(depth, Thermal等)可以有效提高语义分割的准确率,即融合多模态信息可以有效提高准确率。当前方法...
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 ...
论文地址:CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers 代码地址:https://github.com/huaaaliu/RGBX_Semantic_Segmentation 本文贡献: 提出了CMX,一种基于vison-transformer的跨模态融合框架,用于RGB-X语义分割(X为RGB的互补模态); 设计了跨模态特征校正模块(CM-FRM),通过结合其他模态...
This paper makes the first attempt to develop a cross-modal transformer-based crossing intention prediction model merely using bounding boxes and ego-vehicle speed as input features. The cross-modal transformer can leverage self-attention and cross-modal attention to mine the modality-specific and ...
Explore the effects of scaling multimodal CLIP model for cross-modal retrieval.Propose a novel unsupervised contrastive multi-modal fusion hashing network... X Xia,G Dong,F Li,... - 《Information Fusion》 被引量: 0发表: 2023年 Unifying Two-Stream Encoders with Transformers for Cross-Modal Ret...
几篇论文实现代码:《Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning》(CVPR 2021) GitHub:https:// github.com/amzn/image-to-recipe-transformers 《N...
跨模态理解需要对齐输入,从细粒度符号级别 到 粗粒度样本级别。工作的方式就是跨模态的翻译,cross-modal generation, 可以进行模态级别的建模。 所以作者提出的 OPT 模型可以从三个角度进行学习,即:token-level,modality-level,sample-level 进行建模。 从模型结构的角度上来说,主要分为如下三个部分: ...
{Multimodal sentiment analysis is an emerging research field that aims to enable machines to recognize, interpret, and express emotion. Through the cross-modal interaction, we can get more comprehensive emotional characteristics of the speaker. Bidirectional Encoder Representations from Transformers (BERT)...
Multi-modal Self-Supervision from Generalized Data Transformations VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text Video Self-supervised Learning (特指单模态) 目前主要分两类,基于对比学习的(拓展SimCLR, MoCo, BYOL, etc),基于生成式的(ViT+MAE, inpainting, etc...