该文章提出了一种 hierarchical multi-modal contextual attention network (HMCAN)用于多模态虚假新闻检测,可以建模多模态信息同时建模多层次语意关系。 具体来说,我们使用BERT和ResNet分别学习更好的文本和图像表示。然后,我们将获得的图像和文本表示反馈到多模态上下文注意网络中,以融合模态间和模态内的关系
In this paper, we are particularly interested in map query suggestions (e.g., the predictions of location-related queries) and propose a novel model Hierarchical Contextual Attention Recurrent Neural Network (HCAR-NN) for map query suggestion in an encoding-decoding manner. Given crowds map query...
This paper explores the task of visual grounding (VG), which aims to localize regions of an image through sentence queries. The development of VG has signi
In this section, we propose a novel network called Hierarchical Contextual Attention-based (HCA). We first formulate the problem and introduce the basic model of GRU. Then we present the contextual attention-based technique on the input and the hidden state respectively. Finally, we train the ne...
we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations. We further propose an adaptive objective to alleviate the slot imbalance problem by dynamically...
The encoder consists of five convolutional stages progressively downsampling the input image to extract hierarchical features. The final encoder layer feeds into a 12-layer Transformer module. The Transformer’s output initializes the decoder, which upsamples features to match the spatial dimensions of...
The ambition of MGCF is to preserve the multilevel global context features from different hierarchical layers of DLCN. Unlike others by simply concatenating these features, we introduce the information entropy as an attention strategy to enhance useful global context cues. Moreover, considering the ...
Hierarchical neural representation of dreamed objects revealed by brain decoding with deep neural network features. Front. Comput. Neurosci. 11, 4 (2017). Article Google Scholar Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 30 (eds Guyon, ...
PINNACLEgenerates protein representations for each of the 156 cell type contexts spanning 62 tissues of varying hierarchical scales. In total,PINNACLE’s unified multiscale embedding space comprises 394,760 protein representations, 156 cell type representations and 62 tissue representations (Fig.1a). We...
View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums 2022, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) View all citing articles on Scopus ...