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Multimodal Transformer/Ushahidi
Rekodi ya ushahidi wa mbinu

Multimodal Transformer

A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.

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Multimodal Transformer (Cross-Modal Attention-Based Architecture)
Rekodi ya mbinu ya kiajenda · ml-model / deep-learning
  • Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. · URL
  • Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139. · URL
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Taxonomic bucketBERT-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketImage Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultimodal BERT-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSentence Embeddingsmachine-suggested · Relational suggestion, not evidence.Same method familyVision Transformermachine-suggested · Relational suggestion, not evidence.

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Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

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