23-05-24 JTFT NN 2024 A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting None 23-05-30 HSTTN IJCAI 2023 Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer None 23-05-30 Client Arxiv 2023 Client: Cross-variable Linear Integrated Enhanced ...
Secondly, JTSF has fully considered the advantages of joint time-space-frequency filtering. In some cases, noise and speech may be difficult to distinguish in the time domain. But the differences of structural differences turn out to be more obvious in the frequency domain. Thirdly, the JTSF ...
In this paper, an efficient network based on a lightweight hybrid Vision Transformer (LH-ViT) is proposed to address the HAR accuracy and network lightweight simultaneously. This network combines the efficient convolution operations with the strength of the self-attention mechanism in ViT. Feature ...
文本的输入是做完tokenniser的words,加了[CLS]token后直接送到双向的transformer中 文本和视频通过transformer学出来的特征过一个linear layer之后计算余弦相似度。 损失函数如下,NCE loss 模型的一些细节设置: 权重初始化时,video encoder的 spatial attention权重用在imageNet-21K上的Vit权重初始化,temporal attention则...
2024 IJCV Frequency Domain Test-time Forgery Detection with Spatial-Frequency Prompt Learning - 2024 IJCV Frequency Domain WATCHER: Wavelet-Guided Texture-Content Hierarchical Relation Learning for Deepfake Detection - 2024 IJCV Frequency Domain SA3WT: Adaptive Wavelet-Based Transformer with Self-Paced Auto...
Based on joint time and frequency-offset estimation, synchronization detection can be implemented using the correlation peak of the m sequence in low SNR. Theoretical analysis shows the right selection of range for decision threshold. Monte Carlo simulation results show the scheme has good performance...
,nt}(qo,fplateau−qo,ft)where qo,fplateau: a desired plateau rate for the field oil production, qo,ft: field oil production rate at time t. The time scale used in this work is years. Visually, the objective function is aimed to minimize the gray area in Fig. 3. Download: ...
For both image and text encoders, the self-attention block consists of 4 Transformer encoder layers and the MLP block has two fully-connected layers with a ReLU activation layer. The final embedding size of the joint cross-modal space is 2,560. We select the hyper-parameters heuristically ...
Sleep stage classification Time-Frequency Convolutional Neural Network for Automatic Sleep Stage Classification Based on Single-Channel EEG CNN IEEE ICTAI 2017 Sleep stage classification Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain RF Med...
2024 Arxiv EEVG An efficient and effective transformer decoder-based framework for multi-task visual grounding Code 2006 INLGC N/A Building a Semantically Transparent Corpus for the Generation of Referring Expressions Project 2010 ACL N/A Natural reference to objects in a visual domain Code 2012 ...