1. Stacked Cross Attention Network 本文提出了一个 Stacked Cross Attention Network 将 words 和 image regions 映射到一个共同的 embedding space 来预测整张图和一个句子之间的相似性。作者首先用 bottom-up attention 来检测和编码图像区域,提取其 feature。与此同时,也对 word 进行单词映射。然后用 Stacked Cr...
Stacked co-attention networkGraph convolutionFine-grained cross-modal correlationCross-modal retrieval provides a flexible way to find semantically relevant information across different modalities given a query of one modality. The main challenge is to measure the similarity......
This is Stacked Cross Attention Network, source code of Stacked Cross Attention for Image-Text Matching (project page) from Microsoft AI and Research. The paper will appear in ECCV 2018. It is built on top of the VSE++ in PyTorch. Requirements and Installation We recommended the following depe...
CCNet: Criss-Cross Attention for Semantic Segmentation 最近读了两篇attention比较早的在计算机视觉上的论文,不过两篇文章的影响力很大,CCNet的出发点就是在Non-local Neural Network的改进,下面就CCNet这篇论文我做一下自己的理解。 我并不想在上面写一些原话的翻译,我觉得读论文是自己一点一点读的,这样才能有更深...
for the classification tasks, we opted for Categorical Cross-Entropy (CCE) as the loss function, as it consistently outperformed Mean Squared Error (MSE) when paired with the SoftMax output layer. The model was trained on an “NVIDIA GeForce RTX 3090 GPU machine” running Ubuntu with NVIDIA ...
for the classification tasks, we opted for Categorical Cross-Entropy (CCE) as the loss function, as it consistently outperformed Mean Squared Error (MSE) when paired with the SoftMax output layer. The model was trained on an “NVIDIA GeForce RTX 3090 GPU machine” running Ubuntu with NVIDIA ...
Adversarial cross-modal retrieval ACM International Conference on Multimedia (2017) K.Xuet al. Show, attend and tell: neural image caption generation with visual attention International Conference on Machine Learning (2015) R.Kiroset al. Unifying visual-semantic embeddings with multimodal neural language...
CrossrefGoogle Scholar [56] S. Zhang, G. Wang, B. Wang, J. Wang, J. Bai, H. Wang 3D carbon nanotube network bridged hetero-structured Ni-Fe-S nanocubes toward high-performance lithium, sodium, and potassium storage Adv. Funct. Mater., 30 (2020), p. 2001592 View in ScopusGoogle Sc...
Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., Zhang, Q.: Multivariate time-series anomaly detection via graph attention network. CoRR, arXiv:abs/2009.02040, (2020) Tuli, S., Casale, G., Jennings, N.R.: Tranad: Deep transf...
If instead a full 1 Gbps channel is available (i.e. 10× of our network resource assumption), to arrive at the same cross over point, we would need ten times more cores than our computational resource assumption. That equates to 20 cores; such power is available on mainstream servers. ...