GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection With the remarkable success of change detection (CD) in remote sensing images in the context of deep learning, many convolutional neural network (CNN) base... X Song,Z Hua,J Li - 《Internationa...
谷歌发表在 NIPS 2017 上的著名论文《Attention Is All You Need》,提出了一种新型的简单网络架构——Transformer,它完全基于注意力机制,彻底放弃了循环和卷积。 Christopher D. Manning 发表在 EMNLP 2015 上的论文《Effective Approaches to Attention-based Neural Machine Translation》,探讨了两种简单有效的注意机制...
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, Guodong Zhou EMNLP 2019 KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren EMNLP 2019 4.2 Comput...
Ref. [28] extended transformer to the graph input and proposed the graph transformer encoder. In this work, our model is based on the graph transformer encoder. SQL-to-text This technique can leverage automatically generated SQL programs [17] to create additional (question, SQL) pairs, ...
Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery. ISPRS J. Photogramm. Remote Sens. 2022, 190, 196–214. [Google Scholar] [CrossRef] Wu, D.; Zhang, C.; Ji, L.; Ran, R.; Wu, H.; Xu, Y. Forest fire recognition based on...
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...
Replace default CSS transformer and minifier with @parcel/css - Details Replace typeof before dead code elimination to improve bundle size - Details Human readable file size in bundle analyzer report - Details Improve emoji support detection - Details Enable parsing static class initialization blocks ...
GTN ( Yun et al., 2019 ) proposes a novel graph transformer layer which identifies new connections between unconnected nodes while learning representations of nodes. The learned new connections can connect nodes which are serveral hops away from each other but are closely related, which function ...
For the second scene graph generation stage, we provide four different im- plementations covering both convolution and Transformer- based methods. In summary, we make the following contributions to the scene graph community: 1. A new problem: We identify several issues associated with current ...
Vision Relation Transformer for Unbiased Scene Graph Generation Gopika Sudhakaran1,3 Devendra Singh Dhami1,3 Kristian Kersting1,2,3 Stefan Roth1,2,3 1Department of Computer Science, Technical University of Darmstadt, Germany 2Centre for Cognitive Science, TU Darmstad...