Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, ...
rumors, and other misinformation. The spread of such propagandist content in society can lead to fear, uncertainty, panic, or even financial losses in trading markets. Psychological research shows that human beings are only 55–58%
These backbones extracted features from RGB and thermal images, which were fused in a Cross-modality Fusion Transformer (CFT) module to generate enriched features. They reported that their model with the CFT module demonstrated superior performance compared to other experiments conducted. The CFT-...
The loss function that we use is binary cross-entropy (BCE), and we optimize this loss with AdamW (Adam with weight decay) [43], which is often used with transformer-based architecture [35]. We fix a base learning rate of 0.0001 for all experiments and adjust the learning rate scheduler...
An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer. Remote Sens. 2023, 15, 5278. [Google Scholar] [CrossRef] Toshev, A.; Szegedy, C. DeepPose: Human pose estimation via deep neural networks. In Proceedings of the IEEE/CVF Conference on...
As shown in Figure 5, we also applied a gated residual connection that skips over the entire transformer block, providing a direct path to the sequence-to-sequence layer, yielding a simpler model if additional complexity is not required, as shown below: ψ ˜ ( t , n ) = L a y e ...
As shown in Figure 5, we also applied a gated residual connection that skips over the entire transformer block, providing a direct path to the sequence-to-sequence layer, yielding a simpler model if additional complexity is not required, as shown below: ψ ˜ ( t , n ) = L a y e ...