Therefore, we present a spatial-temporal gated graph attention network (ST-GGANet) to learn the spatial-temporal patterns of skeleton sequences. The proposed approach uses a lightweight self-attention-based gate layer to pay attention to the important body parts or joints of human skeleton ...
Interactive Multimodal Attention Network for Emotion Recognition in Conversation The conversational modeling module defines three different gated recurrent units (GRUs) with respect to the context information, the speaker dependency, and ... M Ren,X Huang,X Shi,... - 《IEEE Signal Processing Letters...
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a da...
Predicting taxi demands via an attention-based convolutional recurrent neural network Convolutional recurrent neural networksContext-aware attention mechanismMulti-view spatial–temporal feature extractionAs a flexible public transportation in urban ... T Liu,W Wu,Y Zhu,... - Knowledge-Based Systems 被引...
In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 201...
Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training 使用门控多级注意力和时间对抗训练增强transformer进行视频理解 CVPR2021 Gated Adversarial Transformer (GAT)来提高att... 查看原文 对抗生成网络大集合!论文、视频、代码、研讨会一网打尽! Understanding ...
To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper, a hybrid neural network short-term load foreca...
However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to ...
A GCNN is a non-recurrent network alternative to capture long-term dependencies while avoiding sequential operations for better parallelizability. Thus, recurrent connections typically applied in RNNs are replaced by gated temporal convolutions. In general, convolutional operations are responsible for hie...
21,22 which implemented graph neural network and graph attention network that made use of convolution of neighbouring nodes to significantly improve performance, we design a GRU architecture (henceforth referred to as DNNGRU) which leverages on temporal information to predict OD probabilities. ...