By considering the cross-correlation, we propose a novel multi-head cross-modal attention mechanism to explicitly model the cross-correlation of modal features. The proposed method collaboratively enhances RGB and Flow features through a cross-correlation matrix. In this way, the enhanced features for...
We apply multi-head cross-attention mechanism to hemolytic peptide identification for the first time. It captures the interaction between word embedding features and hand-crafted features by calculating the attention of all positions in them, so that multiple features can be deeply fused. Moreover, ...
Multimodal Sentiment Analysis Based on a Cross-ModalMultihead Attention Mechanism To solve this problem, this paper proposes amodel based on amulti-head attentionmechanism. First, after preprocessing the original data. Then, the feature... L Deng,B Liu,Z Li - 《Computers Materials & Continua》...
Based on it, we explore an interactive graph convolution network (GCN) structure to jointly and interactively learn the incongruity relations of in-modal and cross-modal graphs for determining the significant clues in sarcasm detection. Experimental results demonstrate that our proposed model achieves ...
4.4.8Bimodal Information-augmented Multi-Head Attention (BIMHA) BIMHA[86]consists of four layers. The first layer models the view specific dynamics within the single modality. The second layer models the cross-view dynamics. Wu et al.[86]adopted tensor fusion based approach, which calculates the...
ITF-WPI: Image and text based cross-modal feature fusion model for wolfberry pest recognition 2023, Computers and Electronics in Agriculture Citation Excerpt : We integrate the PSA mechanism in the CoTN network structure of the ITF-WPI model, and to verify the impact of other attention mechanisms...
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval 别实例中存在歧义时,单射嵌入可能会受到影响。考虑一个具有多重含义/意义的模糊实例,例如,多义词和包含多个对象的图像。虽然每个意义/意义都可以映射到嵌入空间中的不同点,但是单射嵌入总是被迫找到一个点...。例如,文本语句可能只描述图像的某些区...
6. MOGONET: Jointly learning the specificity of omics and the correlation of cross-omics after pre-classification using GCN. 7. Combining Transformer encoding modules with GCN to create a novel model for cancer classification. 8. Semi-Supervised SVM (S3VM): This is an extended approach to ...
Figure 3. Effect of the multi-head attention mechanism on model performance of MDASAE. (a) and (b) show the AUC values achieved by MDASAE (with the multi-head attention mechanism) and MDASAE W/O attention (without the multi-head attention mechanism) under the 5-fold cross-...
Dual-encoder transformers with cross-modal alignment for multimodal aspect-based sentiment analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Taipei, 21–23 ...