TransformerMulti-ModalGraph AggregationThe current image captioning directly encodes the detected target area and recognizes the objects in the image to correctly describe the image. However, it is unreliable to make full use of regional features because they cannot convey contextual information, such ...
To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. Taking advantage of the transformer's ability to better integrate multiple information, we design ...
翻译过来的中文题目:AMIGO:用于千兆像素图像表示学习的基于共享上下文处理的稀疏多模态图Transformer 题目中的关键词:基于上下文处理、稀疏、多模态、Graph Transformer 背景: 多实例学习(MIL)已经成为处理WSI组织病理学图像的传统方法。其中,MIL提取WSI图像特征的步骤大致如下:将一张大尺寸的WSI图像(包)切成多个小尺寸的...
AttentionMGT-DTA A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.docx 2.6M · 百度网盘 AttentionMGT-DTA A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.pdf 2.8M · 百度网盘 摘要 药物靶向亲和力(DTA)的准确预测是...
Graph Structure Prefix Injection Transformer for Multi-modal Entity AlignmentOverviewThe overall framework of GSIEA. Dependenciespip install -r requirement.txtDetailsPython (>= 3.7) PyTorch (>= 1.6.0) numpy (>= 1.19.2) easydict (>= 1.10) unidecode (>= 1.3.6) tensorboard (>= 2.11.0)Train...
Multi Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media (AAAI 2024) This repository contains the source code for the mDT Architecture, code to evaluate text-only baselines and how to create the HatefulDiscussions dataset. Our code bas...
Hu J, Qian S, Fang Q, Xu C (2019) Hierarchical graph semantic pooling network for multi-modal community question answer matching. Proceedings of the 27th ACM International Conference on Multimedia, pp 1157–1165 Hu W, Shuaibi M, Das A, Goyal S, Sriram A, Leskovec J, Parikh D, Zitnick...
Secondly, it analyzes unstructured clinical notes by transforming the task into a multi-label classification problem and introduces the use of Label Attention and Attention-based Transformer (LAAT) algorithm to extract informative features from clinical text. To evaluate the proposed model, the freely-...
In this regard, we propose an integrated framework guiding diffusion process at each node by a downstream transformer where both short- and long-range properties of graphs are aggregated via diffusion-kernel and multi-head attention respectively. We demonstrate the superiority of our model by ...
Graph Transformer for Graph-to-Sequence Learning. AAAI 2020. paper Deng Cai, Wai Lam. Multi-‐label Patent Categorization with Non-‐local Attention-‐based Graph Convolutional Network. AAAI 2020. paper Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed Pitera, Jeff Welser, Nitesh Ch...